&EPA
           United States
           Environmental Protection
           Agency
           Environmental Research
           Laboratory
           Athens GA 30605
EPA-600/5-79-009
August 1979
           Research and Development
Costs and Water
Quality Impacts of
Reducing Agricultural
Nonpoint Source
Pollution

An Analysis
Methodology

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                 RESEARCH REPORTING SERIES

 Research reports of the Office of Research and Development, U.S. Environmental
 Protection Agency, have been grouped into nine series. These nine broad cate-
 gories were established to facilitate further development and application of en-
 vironmental technology. Elimination of traditional grouping was consciously
 planned to foster technology transfer and a maximum interface in related fields.
 The nine series are:

       1.  Environmental Health  Effects Research
       2.  Environmental Protection Technology
       3.  Ecological Research
       4.  Environmental Monitoring
       5.  Socioeconomic Environmental Studies
       6.  Scientific and Technical Assessment Reports (STAR)
       7.  Interagency Energy-Environment Research and Development
       8.  "Special" Reports
       9.  Miscellaneous Reports

 This report  has  been assigned to the SOCIOECONOMIC ENVIRONMENTAL
 STUDIES series. This series includes research on environmental management,
 economic analysis, ecological  impacts, comprehensive planning and fore-
 casting, and analysis methodologies. Included  are tools for determining varying
 impacts of alternative  policies; analyses of environmental planning techniques
 at the  regional, state, and  local  levels; and approaches to measuring environ-
 mental quality perceptions, as well as analysis of ecological and economic im-
 pacts of environmental protection measures. Such topics as urban form, industrial
 mix, growth policies, control, and organizational structure are discussed in terms
 of optimal environmental performance These interdisciplinary studies and sys-
 tems analyses are presented in forms varying from quantitative relational analyses
 to management and policy-oriented reports.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.

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                                             EPA-600/5-79-009
                                             August 1979
  COSTS AND WATER QUALITY IMPACTS OF REDUCING
    AGRICULTURAL NONPOINT SOURCE POLLUTION
            An Analysis Methodology
                      by

              Meta Systems,  Inc.
       Cambridge, Massachusetts 02138
            Grant No. R805036-01-0
               Project Officer

              Thomas E. Waddell
Technology Development and Applications Branch
      Environmental Research Laboratory
            Athens, Georgia 30605
      ENVIRONMENTAL RESEARCH LABORATORY
     OFFICE OF RESEARCH AND DEVELOPMENT
    U.S. ENVIRONMENTAL PROTECTION AGENCY
            ATHENS, GEORGIA 30605

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                                 DISCLAIMER

     This report has been reviewed by the Environmental Research Laboratory,
U.S. Environmental Protection Agency, Athens, Georgia, and approved for
publication.  Approval does not signify that the contents necessarily reflect
the views and policies of the U.S. Environmental Protection Agency, nor does
mention of trade names or commercial products constitute endorsement or
recommendation for use.
                                    ii

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                                  FOREWORD

      As environmental controls become more costly to implement and the
penalties of judgment errors become more severe, environmental quality
management requires more efficient analytical tools based on greater know-
ledge of the environmental phenomena to be managed.  As part of this Labor-
atory's research on the occurrence, movement, transformation, impact, and
control of environmental contaminants, the Technology Development and
Applications Branch develops management and engineering tools to help pol-
lution control officials achieve water quality goals through watershed
management.

      Agricultural sources contribute significantly to water pollution
problems in many areas of the United States, but control efforts to reach
water quality goals must recognize the social and economic dimensions of
alternative approaches.  This report presents a technique for analyzing
the water quality and economic impacts of alternative activities and non-
point source pollution control policies as a means of identifying best man-
agement practices.  The methodology should aid the environmental decision-
maker in establishing balanced nonpoint source pollution control policies.

                                      David W. Duttweiler
                                      Director
                                      Environmental Research Laboratory
                                      Athens, Georgia
                                    ill

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                                  ABSTRACT
      This  study addresses the problem of analyzing nonpoint source pollution
 impacts from agriculture.   It was undertaken  to determine the  feasibility of
 developing an analytical method  that can be applied to the assessment of con-
 trols for  reducing nonpoint source pollution  from agriculture. The analytical
 method developed allows the simultaneous examination of 1) the water quality
 impacts of selected agricultural practices and 2) the economic effects that
 alternative practices and nonpoint source pollution control policies have on
 the  farmer.   The nonpoint source pollution control problems that the methodo-
 logy addresses are limited  to those that are  amenable to solution by incre-
 mental on-farm adjustments  for damage reduction.

      The proposed methodology includes  1) a farm model, which  accepts as exo-
 genous inputs alternative agricultural  practices available to  the farmer and
 determines the net revenues resulting from each alternative; 2) a water
 quality model, which analyzes the water quality impacts of the selected agri-
 cultural practices and which is composed of (a) a watershed model that des-
 cribes the pollutants generated by the  farming practices and their impact on
 river water quality and which evaluates soil  loss, and (b) an  impoundment
 model which evaluates the impoundment water quality effects of the watershed
 pollutants;  and 3) a qualitative approach for the assessment of the socio-
 economic impacts of water quality changes on downstream users.  The methodo-
 logy is designed to facilitate the comparison of alternative agricultural
 practices  for the purpose of identifying best management practices (BMP's).
 It also may  be applied to evaluate government nonpoint source pollution con-
 trol  policies and the effects of alternative agricultural futures.  The
 methodology's use for these  purposes is evaluated through an illustrative
 example based on data from  the Black Creek watershed in Northeastern Indiana
 and  a  synthesized downstream impoundment.

   It appears that the development of such a methodology for regional-level
 planning is  feasible and would be of significant value for broad analyses of
 large numbers of policy alternatives, including identification of BMP's. How-
 ever, the methodology is currently at a preliminary stage of development, and
 further refinements are necessary to make it fully operational.

     This report was submitted in fulfillment of Grant No. R805036-01-0 by_
Meta  Systems Inc under sponsorship of the U.S. Environmental Protection Agency.
This report covers the period August 1,  1977 to September 30, 1978, and work
was completed as of September 30, 1978.
                                      iv

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                               CONTENTS

                                                                    Page

Foreword	      iii
Abstract
Figures
Tables  [[[ .......  vii
Acknowledgements  .................................................. viii

     Section 1 .   Introduction ....................................    ]_
     Section 2 .   Conclusions . . ................................ t . t    10
     Section 3 .   Recommendations .,.,,,,, ................... .,,.,,.   15
     Section 4 .   Development of a Farm Model ......................   17
     Section 5.   Water Quality Impact Analysis ....................   23
     Section 6.   Use of Farm and Water Quality Models .............   34
     Section 7 .   Impacts on Downstream Users ............ . t ........   53

References .......................................... t ............   62
Bibliography [[[   55

Appendices
     A.  Farm Model .......................................... .....   77
     B.  Methods  for Predicting Watershed Loadings ............. ...  169
     C.  Methods  for Predicting Impoundment Water Quality  .........  220
     D.  Water Quality Impact Results:  Additional Interpretations
         and Sensitivity Analyses ................................ .  292

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                                   FIGURES
Number
                                                                         Page
   1  Methodology for Assessment of Water Quality Impacts and
        Socio-Economic Impacts of Agricultural Practices	
  la  Use of Methodology for Assessment of Nonpoint Source
        Pollution Control Options Under Alternative Futures.
   2  Schematic View of the Watershed/Impoundment Water Quality
        Analysis	   24

   3  Pathways in the Watershed Analysis	   28

   4  Pathways in the Impoundment Water Quality Analysis	   31

   5  Comparison of Practices — Lowlands	   38

   6  Comparison of Practices — Ridge	   38

   7  Comparison of Practices — Uplands	   38

   8  Effects of Fertilization Rate on Low Yield and River Nitrogen
        Concentrations	   47

   9  Percent Change of Highest Revenue Factor — Lowland	   58

  10  Percent Change of Highest Revenue Factor — Ridge	   58

  11  Percent Change of Highest Revenue Factor — Uplands	   59
                                    vi

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                                  TABLES
Number                                                                   Page

   1  Farm Model:  Elements of Cost and Revenue	  19
   2  Major Features of a Selected Set of Farm Practices in the
        Black Creek Area.
                                                                           20
   3  Summary of Farm Model Output — 1977 Dollars, in Thousands
        (Under Existing Government Policies)	  22

   4  Net Revenue — 1977 Dollars	  35

   5  Impact of Farm Practices on Soil Loss	  3°

   6  Impacts of Farm Practices on Average Annual Concentrations
        of Suspended Solids, Nitrogen, and Phosphorus in the River	  39

   7  Impacts of the Most Erosiv practice  (CB-CV) Relative to the
        Least Erosive (CBWH-NT) on Various Water Quality Components	  40

   8  Impacts of Soil Loss Tax  (1977 Dollars)  (Ridge Farm)	  45

   9  Net Revenue — 1977 Dollars  (Fertilizer Tax Imposed on Nitrogen)...  48

  10  Effect of Future Energy Prices  (Constant 1977 Dollars)	  52

  11  Comparison of Methodologies to Measure Water Quality Benefits	  54

  12  Impacts on Benefit Categories of Water Quality Components	  5&

  13  Relative Impacts of CBWH  Practice on  Water Quality Components
        and Benefit Categories  for the Lowland Soil Type	  6°

  14  Summary of Relative Impacts of Farming Practices on Benefit
        Categories	
                                     via

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                              ACKNOWLEDGEMENTS
    We would like to express our appreciation to the Black Creek Project staff
at Purdue University and the Allen County, Indiana Soil and Water Conservation
District, who provided data and insights in the early stages of our work.
Dr. Klaus Alt, of Iowa State University, was also helpful in providing infor-
mation about his methodology for farm economic analysis.

    We also acknowledge the efforts of the following people, who provided com-
ments on the draft final report: from Purdue University, Darrel Nelson, Harry
Galloway, and Don Griffith of the Department of Agronomy, Edwin Monke, David
Beasley, and William Miller of the Department of Agricultural Economics, and
James Morrison of the Department of Agricultural Information; Jerome L.
Mahloch of the Corps of Engineers, Vicksburg, Mississippi; Daniel J. Basta of
Resources for the Future, Thomas H. Clarke, Jr., of the Council on Environ-
mental Quality; Robert D. Walker of the University of Illinois, and Thomas
0. Barnwell, Jr., of the U.S. Environmental Protection Agency, Athens, Georgia.

    Finally, we would like to thank Mr. Thomas E. Waddell of the Athens
Environmental Research Laboratory for his interest and guidance as project
officer.
                                     viii

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                                  SECTION 1

                                INTRODUCTION
     This study addresses the problem of analyzing nonpoint source pollution
impacts from agriculture.  It was undertaken to determine the feasibility
of developing an analytical method that can be applied to the assessment of
controls for reducing nonpoint source pollution from agriculture.   It is
widely recognized that the goals of the Water Pollution Control Act Amend-
ments of 1972 will be achieved only if in addition to point source pollution,
nonpoint source pollution is controlled. Authority exists under PL 92-500 and PL-
217 for EPA, in conjunction with individual states to devise policies and ini-
tiate control programs to manage nonpoint source pollution.  However, prog-
ress has been slow.  Many reasons can be cited, including strong economic
forces that are in conflict with attempts at environmental control, and the
lack of detailed knowledge of physical, chemical, and biological processes
associated with environmental impact of pollutants from nonpoint sources.
Such knowledge is needed to identify pertinent and defensible policies for
analyzing the impacts of agricultural practices.

     Agri-environmental problems can be classified in various ways.  For this
study we have devised the following classification.

     A.  Problems in which human or ecosystem health is at issue:

         1. those involving residuals generation and transport with
            a large array of chemical transformations over a wide
            temporal scope and near-linear damage functions, such
            as synthetic biocides and toxics;

         2. those involving residuals generation and transport of
            a few defined elements and non-linear  (or threshold)
            damage functions, such as nitrates.

     B.  Problems in which major concern is with aesthetics, recrea-
         tion, or other economic impacts:
         1. those involving generation and transport of residuals,
            such as sediment and nutrients;
         2. those involving long-term land productivity, such as
            soil loss;
         3. those involving spatial diversity, such as monoculture.

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      In solving environmental problems,  at least two different approaches  are
 emerging.   One involves incremental adjustments at local  or  regional  levels,
 while the  other is directed  to controls  at the  national level  after the
 examination of large-scale trade-offs.

      It is believed that the environmental problems of types A-2,  B-l, B-2,
 and B-3 are amenable to solution by incremental approaches based on on-farm
 adjustments to reduce damages.   In this  study we are concerned with the
 development of a methodology focused on  water pollution problems of types
 A-2 and B-l.

      Environmental problems  of type A-l  are not amenable  to  an incremental-
 policy-change approach (i.e.,  on-farm adjustment).   Reasons  include:

      1)  Many synthetic organic chemicals behave largely in an  unknown
         fashion in nature; their persistence and transport through
         food chains and degradation patterns are often not well under-
         stood .
      2)  The risks involved with biocides and toxics may be large and
         are uncertain;  they  involve generations to come as well as
         all persons now living.   Unintended consequences  impact
         other crops,  fields,  times,  and  populations.
      3)  The variety of chemicals makes screening of each  for safety
         difficult.   To prove a chemical  safe oftens requires years
         of testing.
      4)  Damage  is apparently at least linearly  related to dose.

 Taking these  characteristics of biocides and toxics into  consideration,  it
 can be argued that the best  approach to  their control is  one which examines
 the broad  questions of use,  quantity used,  exposure,  potential adverse col-
 lateral  consequences,  etc.,  over time and asks  if  the risks  are worth the
 economic costs  of doing without.

     Methods  of evaluating the  environmental  and socio-economic impacts  of
 agricultural  practices  should exhibit the  following characteristics.

     1) Compatibility between data            4) Ease of  understanding and
        availabilities  and requirements          communications

     2) Robustness against a wide range       5) Usefulness  at the appropriate
        of alternative  agricultural  futures      planning level
     3) Capability of evaluating major        6) Applicability to  the full
        policy options                           range of on-farm  adaptive
                                                 options.

     Based on these characteristics,  the focus of this study is on farm
decision-making  (where crop and technology are decided)  and on aggregation
of the individual decisions to a regional level, rather than on modeled
regional level decision-making where  these decisions  are not made  (but often
wished).

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METHODOLOGY DEVELOPMENT

     Figure 1 is a flow chart of the proposed methodology and provides a
framework for identifying the analytic techniques employed and the data in-
puts required.  It shows 1)  the farm model, which accepts alternative agri-
cultural practices available to the farmer as exogenous inputs and determines
the net revenues resulting from each alternative; 2) the water quality model,
which analyzes the water quality impacts of the selected agricultural prac-
tices and which is composed of (a) a watershed model that describes the
pollutants generated by the farming practices and their impact on river water
quality and evaluates soil loss, and (b) an impoundment model that evaluates
the impoundment water quality effects of the watershed pollutants; and 3)
a qualitative approach for the assessment of the socio-economic impacts of
water quality changes on downstream users.  Each of these is described in
more detail below and in the following sections.  As Figure 1 indicates, the
methodology is designed to facilitate the comparison of alternative agricul-
tural practices for the purpose of identyfing and evaluating best management
practices (BMP's).

     Figure la shows how the methodology may be applied to evaluate govern-
ment nonpoint source pollution control policies and the effects of alterna-
tive agricultural futures.  The control policies and alternative futures are
inputs to the methodology.  Examples illustrating the use of the methodology
for these purposes will be discussed below.

Use of the Illustrative Example

     After completing the literature review for this study, it appeared to
us that the most effective way to approach the determination of the feasi-
bility of developing a methodology would be to work through an illustrative
example.  The example would allow an assessment of the logic and completeness
of the methodology as well as of the requirements for applying the methodology
in a planning context.  In order to minimize required field work and maximize
data available for the example presented in this report, we sought a well-
studied, agricultural watershed with a downstream impoundment.  The latter
was considered necessary for an adequate example of an assessment of water
quality impacts in both flowing and impounded waters.  We were unable to
find a locality meeting all these requirements;  therefore, to implement the
illustrative example, we used the Black Creek watershed in northeastern
Indiana (a U.S. EPA, USDA demonstration project) and synthesized a downstream
impoundment with characteristics typical of those found in the Corn Belt.
Data from impoundments in this region were obtained from the EPA's National
Eutrophication Survey and other sources that permitted regional calibration
of the impoundment water quality models.  The work done on the Black Creek
watershed (see Black Creek Study, Final Report, October 1977) provided a good
source for some of the economic, soils, and water quality data needed for
calibration and illustrative application of the methodology.

     Agricultural Future Scenarios

     The evaluation of environmental control policies for the future  requires
analysis against a predicted structure of agriculture.  The  farmer's decisions

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FIGURE 1:
METHODOLOGY FOR ASSESSMENT  OF WATER QUALITY  IMPACTS AND SOCIO-
    ECONOMIC IMPACTS OF AGRICULTURAL  PRACTICES
GowMMnt
Policies
(See Figure 1A)
Economic
Conditions
(See Figure IA)

Watershed
CHoroderUtlc*
• Geo- Morphologic
• Soil Type
•
Jr
i


General
Agricultural
Practice
Alternative*:
• Crop
• Technology
• Etc.



1
1
1
-r*
1
!_

FARM
Farm
Budget -
Model


WATER QUALITY

MODEL
Economic
Evaluation of
* Agricultural H
Practices
1 	 1
	 _|

MODEL

. 	
Net Re
Practi<


• Geo- Morphologic
• Soil Type
• Slope/Length
• Area
• Cllratotogtc

Impoundment
Choree terletlc*
• Morpheme trie
• Hydrologlc

J!
WATER QUALITY MODEL ] "-•
-rl^o.?^ 1 — •
L



Impoundment
Model







Water Quality
Components
Analyzed for
Alternative
Practices


_J

Water Quality
Impacts for
Net Revenue
Ranked
Practices

Soil Lost
Impact


Qualitative
Comparison of
Downstream ~*°
User Benefit
P

Compare Effects of
Practices:
• Farmer Revenue
and Equity
• Water Quality
Impacts
• Downstream
User Benefits
• Soil Loss


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                         FIGURE  1A:    USE  OF  METHODOLOGY  FOR ASSESSMENT OF NONPOINT SOURCE

                            POLLUTION CONTROL OPTIONS  UNDER ALTERNATIVE FUTURES
 Alternative
 Futures:

 • Price of Energy/
   Labor/Capital

 • Demand for
   Animal/Vegetable
   Protein
Nonpoint Source
Pollution Control
Policy Options:

• Taxes on Fertilizers/
   Biocldes

• Soil  Loss
   Restrictions

• Management
   Practices

• Physical
   Structures
       Is This
   ractlce Acceptable
  Under Alternative
       Futures
       Selection
       of Best
       Management
       Practices
  Is This
  Policy
Superior To
  Other
  Policies
    f
    is This
  Icy Acceptable
Under Alternative
   Futures
      9
         INPUTS
                                       -METHODOLOGY  APPLICATION
                                                                               GOVERNMENT INTERPRETATION •
                                                                                                                     •RESULTS-

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must be analyzed against assumptions regarding the forces driving the agri-
cultural system.

     Without attempting to give a complete list of current trends in modern
U.S. agriculture that have led to the current level of water pollution from
agriculture, we present some of the more important ones.  Because these forces
are affected by government policies and because they affect nonpoint source
pollution, it is important to include consideration of such trends in a
quantitative framework such as the one proposed here.  Some of these broad
national trends can be characterized as follows:

     1) tendency toward larger farm units;
     2) tendency toward absentee ownership (including corporate
        ownership and land speculation);
     3) reduction of direct labor inputs because of rising wages,
        the growth in organized farm labor, and farm capital
        intensification;
     4) large capital investments in machinery manufactured by a
        few firms;
     5) a high degree of market uncertainty because of international
        market integration, in addition to weather and other natural
        phenomena;
     6) emphasis on high yield, single crop farming  (intensive mono-
        culture) ;
     7) increasing utilization of synthetic chemical and nonrenewable
        energy use;

     8) tendency toward non-integration of livestock rearing acti-
        vities, with feed production separated from feedlots;

     9) difficulty of new farmer access to farming and of old
        farmer adjustments to new conditions because of large
        capital stock represented by land, animals, and machinery;

    10) concentration of crop marketing and crop distribution
        activities in fewer and larger firms, including vertical
        integration from farm to retail store;

    11) large federal subsidies to agriculture through irrigation,
        power, flood control, price supports, and research/develop-
        ment extension; and

    12) emphasis on product appearance, ease of mechanical handling,
        and storability.

     Although most of these trends have led to environmental impairment, this
is not to argue that the destructive environmental consequences of U.S. farm-
ing result solely from them.  The destruction of the fragile topsoil of
northern New England more than 150 years ago and the great dust bowls of this
century have had long lasting effects.  Nor can one conclude from these trends
that their attendant social costs necessarily outweigh the benefits

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associated with increased food production.  The point here is that many
national policies and future economic factors influence the range of agri-
cultural practices that will be considered by farmers and hence influence
nonpoint source pollution from agriculture.  Because of time and resources,
we have done little on this aspect of methodology development, and this
represents a serious limitation of this study. However, because of the
uncertainties in the future of agriculture and in order for the methodology
to be flexible and operational, it will be necessary in subsequent work to
evaluate agricultural practices across a broad range of alternative futures.
One such future is continuation of the above trends towards a highly concen-
trated food/fiber production system.  Some believe that such a future, if
achieved, would be unstable.  In Section 6 we discuss briefly other possible
future settings derived from past modifications and extrapolation of current
trends and forces that would influence the environmental impacts from agri-
culture.

     Agricultural Practices and Farm Budgets

     As the first step in this feasibility study, a farm budget is developed
that assumes the current agricultural structure.  A set of agricultural prac-
tices representative of the options available to a farmer in a particular
watershed is selected.  In the example presented in this report, 11 practices
(plus two modifications) are selected, and farm budgets are developed for a
uniform farm of 250 acres on each of the three predominant soils in the Black
Creek watershed.  Timing of operations and agricultural practices such as
livestock integration and organic farming that are important for both farm
revenues and environmental impacts were not considered because of a lack of
available data and the limited scope of this study.

     Water Quality Impacts of Agricultural Practices

     To judge the water quality effects of the agricultural practices, the
water quality  impacts of each practice/soil combination are analyzed as the sec-
ond step in this example. Watershed and water quality analysis is based on the
assumption of homogeneity of the watershed reflected in the farm level analysis.
This is, of  course, illustrative at this preliminary state of methodology
development.  Later use of this method would involve evaluation of the aggre-
gate economic and environmental impacts in a heterogenous watershed.  Thus
in assessing agricultural practices, a watershed is assumed to be comprised
of a number of fields of equal characteristics.  This provides a rough
measure  of  the unit emissions and water quality impacts — impacts of a
given field/soil type/agricultural practice combination as desired in the
assessment of the impact of agricultural practices.  A more realistic evalua-
tion of these practices on a heterogenous watershed (soils, slopes, farm
sizes, and other characteristics) is the next step in the development of a
usuable methodology now that this example of how to proceed with the analysis
of the economic and environmental impacts of various agricultural practices
on a homogeneous watershed has proven feasible.

     This evaluation is designed for assessments of long-term  average water-
shed responses and water quality impacts.  In this analysis the following
water quality parameters are considered.

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                                       r\
     1) impoundment sedimentation  (kg/m -yr),
           a measure of the amount of sediment deposited on the bottom
           of the impoundment per year and thus of the impoundment's
           useful lifetime;

     2) impoundment sediment outflow concentration (kg/m3),
           a measure of the amount of sediment suspended in waters
           withdrawn from the impoundment;
                                                           o
     3) river and impoundment nitrogen concentrations  (g/m3),
           an indication of nitrate levels in the waters;

     4) river light extinction coefficient  (m"1),
           a measure of the resistance to light penetration in the
           river due to turbidity and color;

     5) impoundment light extinction coefficient  (m"1),
           a measure of the resistance to light penetration in
           the surface waters of the impoundment due to turbidity,
           color, and algal growth;

     6) impoundment biomass (g chl-a/m3),
           a measure of the concentration of suspended algae in the
           surface waters of the impoundment during the summers and
           thus a measure of the degree of eutrophication.

For each practice, the watershed models predict average loadings of sediment
 (sand, silt, and clay fractions), nitrogen, phosphorus, and color as functions
of field/soil characteristics.  Transport of water quality components from
the watershed is represented in two phases  (dissolved and  sediment-bound)
and in two streams  (surface runoff and sub-surface drainage).  The water
quality models estimate the impact of these loadings on the average concen-
trations of the respective components in the downstream river and impound-
ments.  Impoundment water quality response is also assessed with regard to
mean summer transparency and chlorophyll-a concentration, which are important
indices of eutrophication.  While water quality impacts are traditionally
assessed with regard to effects of organic loadings  (DOD) on dissolved oxygen
levels, such effects are usually critical for discharges of unstable organic
matter under low-flow conditions.  The impacts considered  in the framework
for analysis developed for this study are more relevant to evaluating the
water quality effects of erosion control practices than are traditional
BOD/DO impacts.

     Impact Assessment and Policy Evaluation

     The third step involves a comparison of the net revenue of each of the
farm practices with the water quality impacts of each practice.  Policies
that would induce those practices that are environmentally advantageous can
then be examined.  Policies considered include:

     1) conservation practice subsidies or requirements;

     2) prohibition of certain cultivation practices?
     3) gross soil loss restrictions;


                                      8

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     4) gross soil loss taxes;

     5) fertilizer limitations or taxes; and

     6) manure/legume subsidies or restrictions.

Government policies that are not instituted specifically for environmental
management purposes — for example, price supports — are regarded as sub-
sumed under definitions of alternative agricultural futures.

     Socio-Economic Impacts of Non-Farm Users

     Finally, a qualitative description of the impacts of different practices
on downstream users is made indicating the direction of the water quality
change in terms of a particular water use and the conflicts among different
users.

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                                  SECTION 2

                                 CONCLUSIONS


     Conclusions are presented under three headings:  1)  methodology;
 2) implementation of a methodology; and 3)  data requirements.

METHODOLOGY

1. The following classification appears useful in considering agro-environ-
mental problems:
   A. Problems where human or ecosystem health is at issue:

     1) those involving residuals generation and transport with an extraordi-
        nary array of chemical transformations over a wide temporal  scope and
        near-linear damage functions, such as synthetic biocides and toxics;

     2) those involving residuals generation and transport of a few defined
        elements and non-linear (or threshold) damage functions; such as nit-
        rates.

   B. Problems where major concern is with aesthetics, recreation, or other
      economic impacts:

     1) those involving residuals generation and transport, such as  sediment
        and nutrients;

     2) those involving long-term land productivity, such as soil loss;

     3) those involving spatial diversity, such as monoculture.

Environmental problems of types A-2, B-l, B-2, and B-3 are amenable to solu-
tion by incremental approaches based on on-farm adjustments to reduce damages.
This study addresses the feasibility of developing methodology focused on
water pollution problems of types A-2 and B-l.

   Environmental problems of type A-l are not amenable to an incremental
policy change approach (i.e., on-farm adjustment), the reasons being:

   • Many synthetic organic chemicals behave largely in an unknown fashion
     in nature;  their persistence, transport through food chains, and degrada-
     tion patterns are often not well-understood.

   • The risks involved with biocides and toxics may be large and are uncer-
     tain; they involve generations to come as well as all persons now living.
     Impacts are on other crops, fields, times, and people than intended.

   • The variety of chemicals makes screening of each for safety difficult.
     To prove a chemical safe may require years of testing.
                                     10

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   • Damage • is apparently linearly related to dose.

As a result of these characteristics of biocides and toxics, it can be argued
that the best approach to their control is one that examines the national
scene and asks if the risks are worth the economic costs of doing without.
Analysis of long-lived residuals might be feasible if data to make the neces-
sary transformations were ever to become available.

2. Methods to evaluate the environmental and socio-economic impacts of agri-
cultural practices should exhibit the following characteristics.
   • Compatibility between data avail-   • Ease of understanding and communi-
     abilities and requirements.           eating.

   • Robustness against a wide range of  • Usefulness at the state level.
     alternative agricultural futures      ,  , .   ,.,..   .   .,   ,- -,,        *
        ,       .. .    ,     . .              • Applicability to the full range of
     and agricultural practices.              _     ,
                                           on-farm adaptive options.
   • Capability of evaluating major
     policy options.

3. To develop a useable method for policy analysis by those responsible for
evaluation and implementation of BMP's, it is necessary to focus on farm
decision making (where crops and technology are decided) and on aggregation
of the individual decisions to a regional level,  rather than on modeled
regional-level decision making where decisions on practices and crops are not
made.  A farm budget approach is thus the appropriate first step in a method-
ology.

4. A broad range of agricultural practices must be evaluated, including live-
stock integration, in order to obtain a full understanding of the range of
environmental impacts and control alternatives.

5. Water quality impacts of different farm practices on different soil types
for sediment, nitrogen, phosphorus, and color can be compared using the
methodology suggested in this report.  It is shown that comparison of prac-
tices based on water quality components in some, but not all, cases leads to
results that are in the same direction (but not of the same magnitude) as
comparisons based solely upon gross soil erosion estimates.  Erosion control
and water quality improvement strategies are not always similar.  In those
cases where the water quality component of greatest importance and gross soil
erosion changes are in the same direction, using soil loss as a proxy measure-
ment for water quality can facilitate the initial evaluation of BMP's.

6. The advantages of using long-term-average time scales for the watershed and
water body response models include:

   • simplified analysis;
   • reasonable data requirements facilitating use of national, regional, and
     local monitoring and experimental data for model calibration  and applica-
     tion;

   • a methodology based in part on existing, well-tested,  and widely applied
     models  (e.g., the Universal Soil Loss Equation);


                                      11

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   •  flexibility and ease of implementation;

   •  response models that are easily understood by decision makers;

   •  response models that are appropriate for assessment of such long-term
      water quality problems as sedimentation and eutrophication.

Nevertheless, use of long-term-average time scales precludes direct assess-
ments of:

   •  watershed and water body responses under extreme meteorologic conditions;

   •  effects of the timing of various agricultural operations  (such as incre-
      mental application of fertilizer);

   •  seasonal variations in water quality induced by normal seasonal varia-
      tions in watershed loadings, which may be particularly important in
      rivers and impoundments with relatively short hydraulic residence times;

   •  analysis of the transport and fate of relatively short-lived compounds.

Modification of the methodology to permit assessments of average seasonal res-
ponses would be feasible without losing many of the above-listed advantages of
a long-term-average approach.  This is because the USLE and the SCS curve
number models, which form partial bases for the assessment, can be applied to
predict seasonal responses.

7. It appears feasible to develop an analytical framework for the evaluation
of alternative agricultural practices in terms of farm economics and water
quality impacts.  The example provided in this report illustrates an evalua-
tion  of a homogeneous watershed.  This study does not include a general appli-
cation of mixed farm operations on heterogeneous watersheds.  It has not
proven feasible to integrate estimation of the socio-economic impacts of
downstream water quality changes (i.e., externalities) into the framework.  A
qualitative presentation of the downstream impacts is possible.  This presen-
tation provides some insight into the possible upstream-downstream conflicts.

8. The literature does not include any examples of theoretically valid bene-
fit estimation methodologies that are directly applicable to the agricultural
non-point source pollution problem.   A number of studies discussed in the
report provide examples of a benefit evaluation that could be applied.  But
such a study would require extensive collection of primary data and would
therefore be expensive to implement.

9. At present the method does not take into account planting time, the timing
of fertilizer and biocide applications,  or harvesting time, all of which are
important in that they affect both farm revenues and the water quality impacts
of different practices.

10.  The methodology can be used to evaluate agricultural practices against
some of the future conditions (e.g., higher energy prices)  that might prevail.
It is important to evaluate alternative practices and policies in light of
alternative future scenarios that are depicted as market product price
changes, unit production factors, or other changes.
                                      12

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IMPLEMENTATION/COMPUTATION

1. The farm budget analysis needs to be automated.  This would allow inclusion
of more farm practices in the evaluation and testing for sensitivity to the
timing of farming activities.

2. An LP model would be useful in asssisting in the evaluation of policies,
once the watershed and water quality models have been refined.

3. The computations involved in performing the water quality analysis are
relatively simple and straightforward.  They can be easily performed with the
aid of a hand calculator or an inexpensive computer program.  Sensitivity and
error analyses are facilitated by the latter.
INPUT DATA REQUIREMENTS

1.  The relatively simple methodology developed to assess water quality
impacts has been shown to allow use of national, regional, and local data
sources for calibration purposes.  Most of the parameter estimates describing
fundamental processes in the watershed and water body would be expected to be
valid at least on a regional basis.  The types of localized (e.g., field or
soil-specific) data required to implement the model are frequently available.

2. A preliminary survey of data availability and the results of sensitivity
analyses indicate that improved estimates of the relative impacts of these
agricultural practices could be obtained through more accurate specifications
of the parameter estimates and/or functional forms used to represent the
following relationships or processes in the watershed/water body response
models:
   a. sediment delivery, as related to drainage basin characteristics and
      sediment texture.
   b. sediment texture, as related to soil texture and erosion rate?
   c. phosphorus trapping in impoundments, as related to sedimentation and
      hydrologic/morphometric characteristics;
   d. the origins and dynamics of dissolved color in watersheds and water
      bodies;
   e. the leaching of dissolved phosphorus from surface crop residues during
      snowmelt (this is particularly important for assessments of reduced til-
      lage alternatives);
   f. seasonal variations in suspended solids and color concentrations in
      impoundments;
   g. turbidity and light extinction in rivers and impoundments, as related to
      suspended solids, color, and algal concentrations;

   h. enrichment of surface soils in phosphorus and organic matter as a func-
      tion of tillage practice;
                                      13

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   i. denitrification in soils, as related to net or total nitrogen input
      rates and soil characteristics.

Some of the needs may be satisfied by a more exhaustive search of the litera-
ture and other data sources; others may require initiation of additional moni-
toring and/or experimental work.

3. Data for the farm budget are largely available for conventional farm prac-
tices, but must be collected on a watershed by watershed basis; some of the
data, such as yield response to fertilizer and biocide application and equip-
ment costs for varying farm sizes, are difficult to obtain and/or derive.
Data for a broader set of agricultural practices that include differing farm
and equipment sizes and livestock integration that can have significant
impacts on water quality are difficult to obtain.

4. Data for benefit evaluation are scarce (or do not exist), preventing reli-
able estimation of a relationship between water quality parameters and value
measurements.

5. More data and analysis are required to provide a basis for interpreting the
chlorophyll-a predictions with regard to the possible harmful effects of
increased eutrophication versus the possible beneficial effects of increased
fish production.  Development and integration of a model for predicting
impoundment dissolved oxygen levels as a function of external and internal
sources of oxygen demand would be helpful.
                                     14

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                                  SECTION 3

                               RECOMMENDATIONS
 1.  Expand  the  number  of  agricultural practices  evaluated  to  include,  for
 example, variations in fertilizer  applications,  timing  of farming activities,
 and livestock  integration,  and  develop  a  classification scheme  for  the  aggre-
 gation of  farms  within a watershed.  These  improvements would describe  the
 watershed  in more  operational  (i.e., realistic)  terms and therefore provide
 greater utility  for evaluating  alternative  BMPs.

 2. Expand the types of policies considered and evaluate the  sensitivity of
 farm net incomes to policy  factors such as the amount of  tax or level of sub-
 sidy.

3. Expand the number and  types of alternative future scenarios considered to
include:
   a.  market product price  changes;
   b.  labor/energy cost  changes; and

   c.  product demand  shifts.

 4. The water quality  assessment should  include:
   a.  Modification of the water quality models to permit  the assessment of
       seasonal-average watershed and water body responses with regard to all
       quality components; transport and fate of relatively stable,  toxic com-
       pounds, including  heavy metals and biocide residues; dissolved oxygen
       responses  in stratified impoundments; and various instream alternatives
       for controlling the impacts  of agriculture on water quality,  including,
       among other  things, sedimentation basins, artificial mixing,  and reser-
       voir operating  policies.
   b.  Additional sensitivity and error  analyses to identify  critical data
       needs within the water quality model  framework.
   c.  A comprehensive search for additional data to satisfy  these needs and
       to identify  processes requiring additional monitoring  and/or  experi-
       mental investigation.
   d.  Empirical  research to further develop data collection  methods for esti-
       mating one or more of the benefit categories, including human health,
       recreation,  or  aesthetics benefits  as related to  physical water quality
       measurements.
                                      15

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5. Investigate the application of methodologies (such as Paretian analysis)
to a qualitative or non-monetary evaluation of the impacts of agricultural
policies affecting water quality in the context of conflicts among interest
groups.
                                     16

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                                  SECTION 4

                         DEVELOPMENT OF A FARM MODEL
     While major market and regulatory pressures — such as prices, taxes,
subsidies, government regulations — are exerted at a regional or national
level, it is the farmer who responds by choosing his crops and methods of
farming.  For this reason the methodology starts with a farm budget.

     We assume the farmer desires to maximize net revenues from the agri-
cultural use of his land subject to judgmental constraints that restrict his
willingness to implement drastic changes that imply unusually high risks.
The farm model does not, for example, depict net revenue if the farmer has
income-producing ventures other than his agricultural operations or if, for
example, he shifts from row and field crops to feed lot operations.  The
farmer chooses a set of agricultural practices that include:

     1) crop rotation;

     2) tillage practices;
     3) structural erosion and drainage control practices;

     4) levels of chemical application.

These choices are represented as inputs to the farm model for the calcula-
tion of a variety of costs associated with operating the farm in the speci-
fied manner.  This required developing a data base for the model.  The
procedure set forth by Dr. Klaus Alt  (See Appendix C, EPA, 1976) was used.1
Each element of cost was updated for 1977 prices and modified where neces-
sary to adapt the model for the Black Creek area.2  The changes were based
on published data for Black Creek and the State of Indiana, opinions of farm
experts in the Black Creek area and at several universities, and information
obtained from farm equipment dealers.
 1Several farm models are available  (e.g., the Purdue Crop Budget).  The Alt
model was selected because it is likely that Appendix C will receive wide-
spread use by agencies involved in the development of BMP's.  Dr. Alt was
most helpful in discussing the adjustment of his model.
 2A11 estimates for the farm model as adapted to Black Creek and the sources
of information used in that process are presented in Appendix A of this re-
port (unattached, available from EPA).  All details associated with the farm
practices, such as types and quantities of fertilizers and biocides, size
and usage of farm implements, including custom hiring and grain drying pro-
cedures, are also contained in Appendix A  (Farm Model).
                                      17

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Additional inputs to the model specify expected yields and market prices for
each crop.  Net revenue is then calculated as follows:
                        Net Revenue =  /  Y P A  - C
                                       f—(  C C C
                                      c=l

where Y  = yield per acre of crop c
       c

      P  = price per unit yield for crop c


      A  = number of acres producing crop c


      n  = number of crops grown in rotation

      C  = cost associated with specified farm practice

Table 1 identifies major categories of cost and revenue data incorporated in
the model.

     Eleven farm practices available to farmers in the case study area were
selected.  These are identified and described in Table 2.  Two of the farm
practices for growing corn, soybeans, wheat, and hay in rotation were ex-
panded.  This was done to include the option available to the farmer of cus-
tom hiring for planting wheat and meadow and harvesting hay.  The custom
hiring alternative was included because it seems unrealistic that a farmer
adopting the farm crop rotation pattern would purchase all the specialized
equipment needed for each crop.

     Each practice was evaluated on three soil types characteristic of the
Black Creek case study area.  These are termed upland, ridge, and lowland
soils.  Different levels of chemical treatment and seeding are associated
with each soil, and crop yields vary.  The definitions of the farm practices
and variations associated with soil type were developed by Meta Systems in
consultation with farm experts involved in the Black Creek project at Purdue
University.

     Because of the limitation of long-term averages in the water quality
analysis, considerations of timing of agricultural operations such as plant-
ing and harvesting were not included.  While the farm budget model, as pre-
sented here, captures the major elements important for assessing the economic
impacts of alternative nonpoint source pollution control policies on the
farmer, further modifications would be necessary before it could be used
effectively in a planning context.  Most importantly, the model should be
automated, perhaps employing a revenue-maximizing linear programming model
for policy analysis.  This would permit explicit consideration of the timing
of farm operations and other factors, and sensitivity analyses would be easy
to perform.   Several automated models, such as the Purdue Crop Budget, are
available and might be adapted to this use.  Nevertheless, we caution that
                                      18

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TABLE 1:  FARM MODEL:  ELEMENTS OF COST AND REVENUE


        Costs                       Revenues


      Terracing                       Corn
      -Construction                   -Yield
      -Maintenance                    -Price

      Machinery                       Soybeans
      -Fixed Cost                     -Yield
      -Maintenance                    -Price

      Tractor                         Wheat
      -Fixed Cost                     -Yield
      -Maintenance and Repair         -Price

      Fuel                            Hay
      -Tractor                        -Yield
      -Combine                        -Price

      Seed
      -Corn
      -Soybeans
      -Wheat
      -Meadow

      Fertilizer
      -Nitrogen
      -Phosphorus
      -Potassium
      -Equipment Rental

      Biocides
      -Herbicides
      -Insecticides

      Labor
      -Direct Labor
      -Overhead

      Other Costs
      -Grain Drying
      -Interest on Operating Capital
                          19

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         TABLE 2:   MAJOR FEATURES OF A SELECTED SET
                            IN THE BLACK CREEK AREA
                           OF FARM PRACTICES
        Crops
Tillage Practice
  Soil        Abbreviated
Conservation  Designation
 Practice       of Farm
               Practice
Continuous Corn (CC)

Continuous Corn (CC)

Continuous Corn (CC)


Continuous Corn (CC)


Continuous Corn (CC)

Corn-Soybean
Rotation (CB)
Corn-Soybean
Rotation (CB)
Corn-Soybean
Rotation (CB)
Corn-Soybean
Rotation (CB)
Corn-Soybean-Wheat-
Hay Rotation (CBWH)


Corn- Soybean-Wheat-
Hay Rotation (CBWH)


Conventional tillage,
fall turn plow (CV)
Conventional tillage,
fall turn plow (CV)
Fall shred stalks,
chisel plow, spring
disk (CH)
Fall shred stalks,
chisel plow, spring
disk (CH)
Fall shred, no till
planting (NT)
Conventional tillage,
fall turn plow (CV)
Fall shred, chisel
plow, spring disk (CH)
Fall shred, no-till
planting (NT)
Fall shred, no-till
planting (NT)
Conventional tillage
fall turn plow for corn;
no-till planting for
soybean , wheat , hay
Fall shred stalks, no-
till planting for all
crops, increased use of
herbicides (NT)
without
terracing
with
terracing
without
terracing

with
terracing (T)

without
terracing
without
terracing
without
terracing
without
terracing
with
terracing (T)

without
terracing


without
terracing

cc-cv

CC-CVT


CC-CH


CC-CHT


CC-NT

CB-CV

CB-CH

CB-NT

CB-NTT

CBWH*
CBWH


CBWH* -NT
CBWH-NT

 Note*. Entry in parentheses used where needed to distinguish specific compo-
nent of farm practice.
 *iridicates farmer-owned equipment for wheat and meadow planting and for hay
mowing, raking, and baling, rather than custom hiring for these operations.
                                       20

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near optimum solutions always be examined with respect to important factors
that may not be incorporated in such a model.

     In applying the farm model three fictitious 250-acre farms representative
of conditions in the Black Creek area of Northeast Indiana are considered.
One farm is on the uplands soil, one on ridge soil, and one on lowlands soil
(the properties of these soils are described in Section 3).  Table 3 shows
the revenues and costs for each of these farms, assuming uniform adoption of
one of the eleven farm practices in the Black Creek area and existing govern-
ment policies in effect.  Highest revenue is achieved with the corn, soybean
cropping pattern and chisel plowing on all three farms.  The revenue from the
corn-soybean rotation with conventional tillage is, however, almost as high
(within two percent).  These and other results from the farm model are dis-
cussed in Section 6.

     The purpose of constructing a farm model is to evaluate agricultural
practices under consideration as Best Management Practices for the impacts on
farm income, water pollution loading, and water quality.  Together with the
proposed government policies designed to encourage these practices, the farm
and water quality models  should be able to incorporate consideration of at
least the following policies:

     1) conservation practice subsidies or requirements;

     2) prohibition of certain cultivation practices;

     3) gross soil loss restriction;

     4) gross soil loss taxes;

     5) fertilizer limitations or taxes; and

     6) manure/legume subsidies or restrictions.

     Investigation of such policies is carried out by 1) modifying the appro-
priate cost or revenue factors in the farm model and recomputing the net
revenues; 2) estimating changes in soil erosion and other water quality im-
pacting parameters; and 3) jointly evaluating the impacts on farm revenues
and water quality.  The use of the farm model in this kind of evaluation is
illustrated in Section 6.

     In addition to evaluating government policies for pollution control, the
farm model can be used to assess future conditions that may have an impact on
the farmer.  Alternative futures can be postulated for government policies
that are not formulated specifically for purposes of environmental management,
such as price subsidies,  Alternative futures might depict changes in econo-
mic conditions, such as increasing prices for energy that affect prices of
fuel used on the farm and purchased farm inputs of fertilizer and biocides.
These changes could alter the farmer's choice of crops, tillage practice,
chemical application and hence induce different impacts on water quality.  An
example of this application of the farm model is also presented in Section 6.
                                      21

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                              TABLE  3:   SUMMARY OF FARM MODEL OUTPUT ~ 1977 DOLLARS, IN THOUSANDS

                                              (UNDER EXISTING GOVERNMENT POLICIES)
to
ro
FARM
\ PRACTICE
REVENUE\
AND COST \
GROSS
F:EVENUE
A. UPLAND
SOIL
B. RIDGE
SOIL
C. LOWLAND
SOIL
COSTS
A. UPLAND
SOIL
B. RIDGE
SOIL
C. LOWLAND
SOIL
NET
RETURN
A. UPLAND
SOIL
B. RIDGE
SOIL
C. LOWLAND
SOIL
TILLAGE PRACTICES
CORN,
CONVEN-
TIONAL
TILLAGE
(cc-cv)

52.5
65.0
65.0

39.7
11.1
12.7

12.8
23.6
22.3
CORN,
CHISEL
PLOW
(CC-CH)

52.5
65.0
65.0

39.1
10.9
12.1

13.1
21.1
22.9
CORN,
NO-TILL
(CC-NT)

49.9
65.0
52.0

13.0
11.9
15.5

6.9
20.1
6.5
CORN,
SOYBEAN,
CONVEN-
TIONAL
TILLAGE
(CB-CV)

16.3
59.1
59.1

32.9
33.3
31.8

13.5
25.8
24.1
ROTATIONS
CORN
SOYBEAN,
CHISEL
PLOW
(CB-CH)

46.3
59.1
59.1

32.6
33.1
34.5

13.7
26.1
21.6
CORN
SOYBEAN,
NO-TILL
(CB-NT)

11.4
57.9
50.7

32.3
32.7
31.1

12.2
25.1
16.6
CORN,
SOYBEAN,
WHEAT, HAY,
PARTIAL USE
OF HERBICIDES
(CBWH*) (CBWH)

13.0
51.8
19.9

34.4
34.7
35.1

8.5
17.1
11.5

13.0
51.8
49.9

30.6
31.0
31.7

12,4
20.8
18.1
CORN, SOY-
BEAN, WHEAT
HAY,
NO-TILL
(CBWH -NT)(CBWH-NT)

43.0
51.8
49.9

31.2
31.1
35.1

8.8
17.1
13.9

13.0
51.8
19.0

30.3
30.7
31.1

12.8
21.1
17.6
TERRACES
CORN,
CONVEN-
TIONAL
TILLAGE
(CC-CVT)

56,0
68.5
68.5

16.1
18,2
19.1

9.6
20.3
19.1
CORN,
CHISEL
PLOW
(CC-CHT)

56.0
68,5
68.5

15.8
47.6
48.9

10.2
20.9
19.6
CORN,
SOY-
BEAN,
NO-
TIUL
(CB-NTT)

47.1
60.9
53.7

39.9
39.3
40.7

8.6
21.5
13.0
          NOTE:  COLUMNS MAY NOT ADD DUE TO ROUNDING.
'INDICATES CUSTOM HIRING.

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                                   SECTION  5

                        WATER QUALITY IMPACT ANALYSIS
INTRODUCTION

     The next step in the methodology involves development and use of mathe-
matical models to provide quantitative means of estimating the water quality
impacts of agricultural practices.  The development of these models is de-
scribed in detail in unattached Appendices B, C, and D of this report.  The
models have been calibrated and applied to assess the changes in water qual-
ity resulting from implementation of 11 farm practices described in Section 4
on each of three field/soil types.

     Figure 2 depicts the separation of the water quality analysis into two
major sections:

     1) the watershed.or runoff model, which is characterized as generat-
        ing different loadings of pollutants depending on agricultural
        activities and watershed characteristics.

     2) the impoundment, where water quality is dependent upon the type
        and quantity of loadings from the watershed and upon impoundment
        characteristics.

In this scheme the river is represented as a medium for transporting the pol-
lutant loadings from the watershed to the impoundment.  Water quality condi-
tions in the river reflect these loadings, which enter the river in surface
runoff and groundwater base flow and are transported in dissolved and sedi-
ment-bound phases.  River water quality is estimated at the point of entry
into the impoundment.  Pollutant losses in overland flow and river transport
are aggregated.

     The water quality impact analysis includes the following components that
may influence the suitability of waters for beneficial uses:

     1) sediment (suspended solids, turbidity);

     2) phosphorus;

     3) nitrogen;

     4) dissolved color;

     5) transparency (as influenced by turbidity, color, and algal
        growth);

     6) algal growth  (as measured by chlorophyll-a concentration).


                                      23

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                                          Regional Climatologic
                                             Characteristic s
     ffatershed/Soil
     Characteristics!
to
Agricultural
Practice
Characteristics
                           Watershed
                             Model
  Watershed Emissions
    and River Water
Quality Characteristics
   -suspended solids
   -nitrogen
   -phosphorus
   -color
                                                                     Impoundment
                                                                        Model
Impoundment Water
Quality Characteristics
   -suspended solids
   -nitrogen
   -phosphorus
   -color
   -transparency
   -chlorophyll-a
                                                            Impoundment Morphometric/
                                                            Hydrologic Characteristics
          FIGURE 2:  SCHEMATIC VIEW OF  THE WATERSHED/IMPOUNDMENT WATER QUALITY ANALYSIS

-------
Dissolved oxygen, biocide residues, and biocides are additional water quality
components relevant to the analysis of water quality impacts of agricultural
practices that have not been included in the framework.  The model framework
could be adapted to consider dissolved oxygen in stratified impoundments as
influenced by external and internal (photosynthetic) organic matter loadings.
While it is not feasible at this time to model effectively the behavior of
relatively short-lived biocides in the type of framework developed here, con-
sideration of relatively stable biocides and biocide residues may be possible
if and when basic data are available.  This possible modification is left for
future work.

     The model framework described below should not be viewed as a static or
final form, but as a preliminary and evolving one.  Application of sensitivity
and error analysis techniques to the framework will serve to guide future
efforts at refining the methodology.  Such efforts would include:

     1) obtaining and analyzing addi-   4) considering different time scales
        tional data for parameter          for averaging? and
        estimation;                     _.     . ,   •    **•!.•   -,
                                        5) considering additional components.
     2) modifying several functional
        forms;

     3) including additional inter-
        actions or mechanisms;

It is apparent that a variety of approaches could be taken in modeling the
behavior of the water quality components in watersheds, rivers, and impound-
ments.  Prior to describing the specifics of our approach, it would be appro-
priate to discuss briefly the factors that were considered in selecting or
formulating the models.


BASIS OF MODELING APPROACH

     In selecting a modeling approach to the physical land-water interface,
factors related to both defining the overall project goal and performing the
particular analysis have to be considered.  Without entering a lengthy dis-
cussion, we would like to briefly document our approach to the model selec-
tion process.

     Two points that impact the selection of models are related to the pro-
jects 's goals.

     • Applying models in a policy-making context requires availability
       of flexible and operational models.  Quick computation and
       recomputation of the impacts of alternative  settings  (i.e.,
       scenario/policy/practice mix) can only be accomplished if a low-
       cost operational tool is available whose input requirements are
       limited.

     • Given the goals of improving/developing a methodology for evaluat-
       ing management practices in terms of water quality  impact,  it  is
       necessary to include all the processes and parameters of  the
                                      25

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      land/water interface related to different farm practices and esti-
      mation of those water quality components relevant to existing and
      anticipated future standards or criteria.
      The premise of our approach is that no single model can adequately cap-
 ture the land/water interface (Meta Systems, 1976):   aspects of the interface
 have to be modeled separately,  and the models have to be linked up in a homo-
 logous way.  Literature exists  on problems encountered in developing models,
 linking models describing various processes, and making use of various data
 bases originally not coordinated for the same purpose.  It is therefore impor-
 tant to select, develop,  or modify models in such a  way that they are compat-
 ible with one another.   Meta Systems (1976)  has elaborated factors relevant
 to evaluating the appropriateness of models for their inclusion in linkages
 of models.  These range  from justifications of models in terms of the robust-
 ness of their quantitative depictions of physical processes to the ease of
 directly connecting models.   We  feel that the following factors have parti-
 cular importance for this study.

      • Complex simulation programs whose application and execution re-
        quire extensive  resources  (computers,  data, manpower,  etc.)
        usually are not  suitable  for policy analyses  that require a
        large amount of  separate  applications.   Should a study demand
        predictions of "short-term" conditions,  such  as runoff and wash-
        off,  because of  single precipitation events,  then it is clear
        that these types of models would  be necessary.
      • Complicated models often do not result in  reliable and useful
        results,  considering  the difficulties  and  expense involved in
        1) estimating parameters;  2)  providing boundary conditions;
        3) testing.

      • While "complicated" models may provide  more "handles"  for policy
        evaluation and permit substitution of  fundamental theory for  lack
        of empirical data,  the theory in  this  area is  rather primitive,
        implying  that the  value of these  models  is still somewhat low.

      •  Interpretations of short-term, event-based simulations are more
        difficult  because  they require an arbitrary event definition.

     •  Given available sources of  national and  regional data  (EPA/NES,
        USDA, etc.),  we find  it desirable  to make  as much use  as  pos-
        sible of these data in addition to possible local  data sources
        (generally limited) (Walker, 1977;  Reckhow, 1977; Meta Systems, 1976).

     To test the  feasibility of a  framework for economic/physical analysis of
agricultural practices,  it was necessary  to start with  a  relatively  simple
methodology that yields long-term or seasonal average results; otherwise, the
problems associated with complicated models would dominate  the analysis and
detract from the major task.  Our conclusions on  feasibility  rest on this
simple  approach.  We  feel that given currently available  data and knowledge
of the  relevant physical processes, a framework built from  complex models
would not be feasible or useful in a planning context.
                                     26

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METHODS FOR PREDICTING WATERSHED EMISSIONS
                                          1
     The methods developed to assess the impacts of agricultural practices on
nonpoint pollutant loadings are of an empirical nature and are concerned with
long-term average emissions, in the spirit of the Universal Soil Loss Equa-
tion (Wischmeier and Smith, 1972).  Average export rates of the following
substances are evaluated in surface runoff and in subsurface drainage:

     1) Sediment (sand, silt, and clay      3) Dissolved nitrogen; and
        fractions);                          „.
                                            4) Dissolved color.
     2) Phosphorus (NH^F/HCl) extractable
        particulate and soluble);

The computed concentrations of these components are assumed to be representa-
tive of average water quality conditions in rivers draining the agricultural
watershed.  This part of the methodology is appropriate for linking with
downstream models for the purpose of evaluating quality impacts in impounded
waters.

     Watershed emissions or loadings are computed as functions of the follow-
ing characteristics:

     1) Surface Soil Properties
        a. Erodibility  (K factor in USLE, Wischmeier and Smith, 1972)
        b. Texture (sand, silt, and clay content)
        c. Hydrologic Soil Group (SCS/USDA, 1971)
        d. NHifF/HCl extractable phosphorus content  (in each texture class
        e. Phosphorus distribution coefficient  (g extractable P/Kg soil)/
              (g dissolved P/m3 soil solution)
        f. Organic matter content (in each texture class)

     2) Watershed/Field Properties

        a. Slope
        b. Slope length
        c. Surface area
        d. Total flow  (runoff and drainage)
        e. Rainfall erosivity  (R factor in USLE)

     3) Agricultural Practices
        a. Cropping factor  (C in USLE)
        b. Practice factor  (P in USLE)
        c. Nitrogen and Phosphorus fertilization rates
        d. Tillage depth
        e. Crop residue management

Pathways involved in the watershed model are  depicted in Figure 3. A brief
summary of the essential features of this framework is given below.
      Appendix B.
                                      27

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      WATERSHED
      CHARACTERISTICS

       Field Characteristics
       Soil Characteristics
       Climate
       Morphometry
       Agricultural
        Practices
       Crop Yields
TRANSPORT RATES

  Sediment
  Runoff
  Percolation
TRANSPORT  MEDIA
COMPOSITION
  Sediment
  Runoff
  Percolation
                              Nitrogen Budget
AVERAGE RIVER
WATER QUALITY
AND COMPONENT
LOADINGS
  Sediment
  Phosphorus
  Nitrogen
  Color
              FIGURE 3:   PATHWAYS IN THE WATERSHED ANALYSIS


     Gross erosion  estimates  are based upon the Universal Soil Loss Equation
 (USLE), which has been  developed by the USDA for use in the soil conserva-
 tion area.  To make the equation more useful as a tool for evaluating water
 quality impacts, explicit  consideration is given to sediment texture varia-
 tions.  Since the finer fractions of soil generally have higher surface
 areas per unit mass,  they  have higher adsorption capacities for various
 water quality components.   By separately considering the clay, silt, and  sand
 fractions in  surface  soil  and eroded sediment, differences in the behavior
 and transport of these  size fractions and their adsorbed pollutants are ex-
 plicitly represented, both in the watershed and in the impoundment systems.
 Applying a separate delivery  ratio for each texture class permits estimation
 of sediment and adsorbed pollutant transport to the impoundment.

     In each  texture  class the phosphorus and organic matter contents of
 sediment particles  are  assumed to equal those in the corresponding size
 fraction of surface soil.   Because of shallower mixing depths, reduced til-
 lage methods  can cause  enrichment of surface soils in nutrients and organic
matter.  These dependencies are explicitly considered in the model framework.
Extractable phosphorus  contents of the clay, silt, and sand fractions are
computed as functions of the  respective background levels, fertilization
rates, and tillage  depths.  Similarly, organic matter contents are computed
 from background levels,  crop  residue additions, and tillage depths.  The
computed compositions and  delivery rates of sediment in the various size
 fractions are used  to estimate the sediment-bound loadings of these compo-
nents.

     Flow from the  watershed  consists of two components:  surface runoff  and
 subsurface drainage.  The  sum of the two is assumed to be independent of  soil
 type or agricultural practice.  This is essentially equivalent to assuming
 that average  evapotranspiration rates are independent of these factors.
 Surface runoff is estimated based upon region, Hydrologic Soil Group  (SCS/
USDA, 1971),  and farm practice using methodology developed by Woolhiser
                                      28

-------
(1976, also EPA/USDA, 1975).   The latter is based upon hydrologic simulations
using the SCS Curve Number model (SCS/USDA, 1971).   Drainage is estimated as
the difference between total flow and surface runoff.

     Predictions of surface runoff and drainage are used to estimate the
transport of dissolved phosphorus and color.  Linear adsorption isotherms are
employed to estimate 1) the dissolved phosphorus concentration in surface
runoff from the average extractable phosphorus content of eroded sediment,
and 2) the dissolved color concentration in surface runoff from the average
organic matter content of eroded sediment.  Dissolved phosphorus and color
concentrations in drainage are assumed to be constant at relatively low
values (0.3 g/m3 and Om"1 , respectively) because they are in equilibrium with
subsurface soils which are deficient in extractable phosphorus and organic
matter.

     In addition to the sediment-bound and soluble phosphorus loadings, ex-
plicit consideration is given to the potential for leaching of phosphorus
from surface crop residues during snowmelt periods.  Because of frozen soil
conditions, dissolved phosphorus in snowmelt may not equilibrate (i.e., be
adsorbed by) surface soils.  Timmons, et al.  (1968, 1970) have shown this
component to be potentially important when compared with other soluble phos-
phorus losses from agricultural watersheds.  Despite the relative lack of
data in this area, leached residue phosphorus has been included because it
may be important to evaluate the impacts of minimum tillage methods which
tend to create a high potential for such losses by leaving crop residues on
the soil surface.

     Because nitrogen  is generally more mobile in soil systems than phos-
phorus, estimates of average soluble nitrogen export are based upon mass
balance rather than upon computed soil erosion rates and adsorption chemistry.
The input terms in the mass balance include fixation, fertilization, precipi-
tation, and soil mineralization.  The output terms include crop yield,
denitrification, and losses in runoff and drainage.  For each soil type and
practice, various data sources are used to estimate the net nitrogen input
rate, which is defined as the total input minus crop yield.  For each soil
type, denitrification  is estimated as a constant fraction of the net input
rate.  The total loss  in runoff and drainage is then estimated by difference.
This scheme ignores export of particulate nitrogen, which is assumed to be
not as important as a  nutrient source or water quality component (see Appen-
dix B, unattached).

     The methodology described above is applicable to a  single field or plot
of uniform characteristics. In preliminary assessments of agricultural prac-
tices, a hypothetical  watershed is assumed to be comprised of a number of
fields of equal characteristics.  This provides a rough measure of the unit
emissions and water quality impacts of a given field/soil type/agricultural
practice combination.  The methodology could be  applied  as well  to a hetero-
geneous watershed consisting of a number of areas, each  with  its own set  of
field/soil type/practice specifications.   The effects of heterogeneous water-
shed characteristics on practice evaluations  and conclusions  are considered
                                      29

-------
 higher  level  questions  which would  be  addressed  subsequent  to  analysis  of
 homogenous  watersheds.

      In order to conform  to  an  economic  analysis,  the  watershed model is
 calibrated  to three  different field/soil types which are  characteristic of
 the  Black Creek Watershed, Indiana.  A research  and demonstration program
 sponsored in  that watershed  by  the  EPA (Christenson and Wilson, 1976; Lake
 and  Morrison,  1975)  has provided  some  data  necessary for  calibrating the
 models.  On each soil type,  the watershed model  is calibrated  for evaluation
 of 11 agricultural practices.   Details of the calibrated  procedures and
 results are discussed in  Appendix D.


METHODS FOR PREDICTING IMPOUNDMENT WATER  QUALITY2

      In tune  with the watershed models,  the framework  developed for assessing
 impoundment water quality impacts consists  of empirical models which are
 designed to predict  steady-state, seasonal, or long-term  average conditions.
 The  following water  quality  components are  considered:

   1) sediment concentrations and trap-   4) mean  summer, Secchi Disc trans-
      ping  rates                             parencies
   2) phosphorus concentrations and       5) mean  summer, epilimnetic chloro-
      trapping rates                        phyll-a concentrations
   3) nitrogen concentrations and trap-
      ing rates

 Models  are  formulated for each  of the  above components based upon theoretical
 considerations and the  results  of previous  modeling efforts.   They are
 calibrated  and tested empirically using  a data base characterizing the  beha-
 vior  of  these  components  in  Corn  Belt  impoundments and compiled from various
 sources  (EPA/NES, 1975; USDA, 1969; ISBH, 1976; USAGE, 1977).

      The sensitivities  of the above water quality  components are assessed
 with  respect  to annual  average  input rates, or loadings,  of the following
 substances:

   1) water                               4) nitrogen

   2) sediment (sand, silt,  and clay)     5) dissolved  color

   3) phosphorus (total soluble and
      extractable particulate)

Additional  independent variables  of importance include mean depth and im-
poundment type (reservoir versus  natural  lake).  The pathways  in the
 impoundment water quality analysis are summarized  in Figure 4.  Essential
 features are discussed below.
  o
   See Appendix C
                                      30

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    LOADINGS
   Color
   Sediment
   Phosphorus
   Nitrogen
Color
                       OUTFLOW/
                       EPI LIMNETIC
                       CONCENTRATIONS
                                                                  Transparency
                                                                  Chlorophyll-a
                                                                  Concentration
                    IMPOUNDMENT MORPHOMETRIC
                    AND HYDROLOGIC CHARACTERISTICS
        FIGURE 1:  PATHWAYS IN THE IMPOUNDMENT WATER QUALITY ANALYSIS

     Following the watershed model, the behavior  of the  sand,  silt,  and clay
fractions of sediment are modeled separately within the  impoundment.   A modi-
fication of Bruyne's  (1953) empirical curves is used to  estimate  the trapping
efficiency of sediment in each texture class as a function  of  mean hydraulic
residence time.  Bruyne's curves are represented  reasonably well  by  a  model
which assumes a  first-order decay process  for sediment in a completely-mixed
system.  Decay rate parameters for clay and silt  are selected  to  match
Bruyne's lower and upper envelope curves,  respectively.  The sand decay rate
parameter is selected so that essentially  all of  the influent  sand is  trapped.
Total sedimentation rate and outflow suspended solids concentration  are esti-
mated as the respective sums over texture  classes.

     The retention, or trapping, of phosphorus is represented  by  an  empirical
model which is calibrated using data on phosphorus budgets  and sedimentation
rates provided for a cross-section of 15 impoundments by the EPA's National
Eutrophication Survey (1975) and the USDA  (1969). Data  indicate  that  the
"effective settling velocity"  (Vollenweider, 1969) for total phosphorus in
these impoundments is a strong function of sedimentation rate.  This suggests
that adsorption/sedimentation reactions represent important phosphorus
removal mechanisms in these impoundments.   The settling  velocity is  also
weakly correlated with mean depth and surface overflow rate.   Average outflow
phosphorus concentration is estimated from a steady-state  mass balance, based
upon the average inflow concentration and  computed trapping efficiency.
                                      31

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Average outflow concentrations are related to median, summer concentrations
measured within the impoundments using empirical relationships derived from
50 EPA/NES impoundments in the Corn Belt.

     The development of models for nitrogen trapping and outflow concentra-
tion follows that of phosphorus.  Data suggest, however, that, unlike phos-
phorus trapping, nitrogen trapping is not significantly dependent upon sedi-
mentation rate.  The nitrogen trapping model is calibrated using data from
50 EPA/NES impoundments.  These impoundments are considerably less efficient
in trapping nitrogen than in trapping phosphorus.  In the 50 impoundments
studied, the average nitrogen and phosphorus retention coefficients are  .24
and .44, respectively.  This is partially attributed to the fact that average
nitrogen loadings are roughly three times in excess of phosphorus loadings,
relative to algal growth requirements.  This conforms to the results of EPA/
NES bioassay studies, which indicate that, given adequate light, algae in
most of these impoundments are phosphorus, as opposed to nitrogen limited.

Based upon data from eight impoundments provided by the Indiana State Board
of Health, Secchi Disc transparency is represented as being inversely propor-
tional to the visible light extinction coefficient in the water column.
Light extinction is attributed to the following:  1) water; 2) dissolved
color; 3) non-algal, suspended solids; and 4) algal suspended solids  (repre-
sented by chlorophyll-a concentration).  The first term is a constant; the
last three are represented as linear functions of the respective concentra-
tions.  These relationships are calibrated using data from the region and
the general literature.  Estimates of dissolved color are based upon the
color loadings derived from the watershed model, assuming a first-order decay
mechanism for color within the impoundment.  Suspended solids concentrations
are derived directly from the sedimentation model.  Mean summer chlorophyll-a
concentrations are estimated using the method described below.  The applica-
tion of a seasonal correction factor to the average annual outflow color and
suspended solids concentrations permits estimation of mean summer light
extinction coefficients and Secchi Disc transparencies.

     Chlorophyll-a is used as an index of primary production, trophic state,
and, in some systems, fish production.  The model developed for predicting
chlorophyll-a levels considers the possible effects of algal growth limita-
tion by light, phosphorus, and/or nitrogen.  Expressions for the maximum
biomass levels limited by each of the above factors are based upon steady-
state solutions of theoretical equations describing algal growth in a mixed
surface layer.  For a given region and climate the light-limited biomass
level is sensitive to epilimnion depth and the portion of the visible light
extinction coefficient attributed to water, color, and non-algal suspended
solids.  The phosphorus- and nitrogen-limited levels are dependent upon
mean summer concentrations of total phosphorus and total nitrogen, respec-
tively, in the epilimnion.  These limiting biomass expressions are combined
in an empirical form to allow for simultaneous limitation of algal growth
by more than one factor.  The model is calibrated and tested using data from
50 impoundments in the Corn Belt.  Analyses of residuals, tests for para-
meter stability, and evaluations of model performance on an independent data
set of 20 impoundments are offered as evidence of model verification.
                                     32

-------
     The calibrated impoundment model has been linked with the watershed
model to create a framework for assessing the effects of the 11 different
agricultural practices on each of three soil associations in the watershed.
Additional factors which must be specified for the assessment include total
watershed area, impoundment surface area, and impoundment mean depth.  Values
of 200 km2, 5 km2, and 4m, respectively, have been selected as. being typical
of watershed/impoundment configurations in the data set used to develop the
impoundment models.  With a total flow rate of .25 m/yr from the watershed,
the hypothetical impoundment has a surface overflow of 10 m/yr and a mean
hydraulic residence time of .4 years.

     It should be noted that our evaluations of the relative impact of the
practices on impoundment water quality may be somewhat sensitive to this
choice of a watershed/impoundment configuration.  The methodology could be
applied as well to alternative configurations.  Because the watershed model
is concerned with long-term average loadings, the analytical framework may
be less valid for application to impoundments with extremely short hydraulic
residence times in which seasonal variations in loading may be important.
                                      33

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                                   SECTION 6

                    USE OF FARM AND WATER QUALITY MODELS


     The results derived in this section are for illustrative purposes and
are based on the analytic processes described in the previous sections.  In
presenting the examples, our intent is to show how the joint use of the farm
and water quality models could serve as analytical tools in the development and
evaluation of BMP's.  The two models are used to illustrate 1) how agricultural
practices can be evaluated under existing policies and 2) how government poli-
cies could affect the implementation of these practices so that they are con-
ducive to water quality improvements. The evaluation of agricultural practices
under current policies uses the 11 selected farm practices listed in Table 2
(as if they constituted a comprehensive set of alternatives currently avail-
able to farmers) and shows how the practices impact farm revenues and water
quality.  These results provide the reference conditions from which alterna-
tive policies can be identified and evaluated.  Shifts in policies aimed at
improving water quality can affect farm revenues and may require government
actions such as subsidies, taxes, or restrictions on certain agricultural
practices or farm implements.   The policies illustrated in this section con-
cern reduction of soil loss and river nitrogen.  Future economic conditions
that affect the farmer — apart from environmental regulations — can also
be incorporated in the evaluation by adjusting the farm model.  An example
is presented showing the impacts of increased energy costs.


CURRENT PRACTICES

     Table 4 shows the ranking of the 11 selected farm practices in terms of
net revenues for the three farms.  The corn-bean-wheat-hay rotation using
all farmer-owned equipment has been dropped from the evaluation in favor of
custom hiring for wheat and meadow planting and hay harvesting.  Use of the
farmer-owned equipment option would obscure the merits of the four-crop
rotation alternative.  The corn-soybean rotations are most profitable based
on prices chosen for these commodities in the illustration (i.e., corn,
$2.00 per bushel; soybeans, $5.00 per bushel; wheat, $2.50 per bushel; hay,
$60 per ton).   The chisel plow tillage method would be selected over conven-
tional tillage with a moldboard plow.  The maximum profitability for the
three farms ranges from $26,100 (the ridge farm)  to $13,700  (the uplands
farm).

     Table 5 ranks the farm practices for the three farms according to soil
loss (gross erosion).  For the uplands farm the practice which maximized net
revenue results in an annual soil loss of 15.2 tons per acre.  On this farm
losses range from 27.2 tons per acre for corn-soybean rotation with conven-
tional plowing (CB-CV)  down to 2.7 tons per acre for corn-soybean-wheat-hay

                                     34

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                            TABLE 4:  NET REVENUE — 1977 DOLLARS
Uplands Farm Ridge Farm
Farm Practice $ Rank $ Rank
Continuous Corn, Conventional Tillage,
without Terracing (CC-CV) 12,800 4 (Tie) 23,600 5
Continuous Corn, Conventional Tillage,
with Terracing* (CC-CVT) 9,600 9 20,300 10
Continuous Corn, Chisel Plowing, with-
out Terracing (CC-CH) 13,400 3 24,100 4
Continuous Corn, Chisel Plowing, with
Terracing (CC-CHT) 10,200 8 20,900 8
Continuous Corn, No-Till Planting,
without Terracing (CC-NT) 6,900 11 20,100 11
Corn-Soybeans, Conventional Tillage,
without Terracing (CB-CV) 13,500 2 25,800 2
Corn-Soybeans, Chisel Plowing, with-
out Terracing (CB-CH) 13,700 1 26,100 1
Corn-Soybeans, No-Till Planting,
without Terracing (CB-NT) 12,200 7 25,100 3
Corn-Soybeans, No-Till Planting, with
Terracing (CB-NTT) 8,600 10 21,500 6
Corn-Soybeans Wheat-Hay, Conventional
Tillage for Corn only, without Terrac- 12,400 6 20,800 9
ing (CBWH)
Corn-Soybeans Wheat-Hay, No-Till
Planting, without Terracing (CBWH-NT) 12,800 4 (Tie) 21,100 7
Lowlands Farm
$ Rank
22,300 4
19,100 6
22,900 3
19,600 5
6,500 11
24,400 2
24,600 1
16,600 9
13,000 10
18,100 7
17,600 8
*PTO Terraces.

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                            TABLE 5:  IMPACT OF FARM PRACTICES ON SOIL LOSS
cr>

Farm Practice

Continuous Corn, Conventional Tillage,
without Terracing (CC-CV)
Continuous Corn, Conventional Tillage,
with Terracing (CC-CVT)
Continuous Corn, Chisel Plowing, with-
out Terracing (CC-CH)
Continuous Corn, Chisel Plowing, with
Terracing (CC-CHT)
Continuous Corn, No-Till Planting,
without Terracing (CC-NT)
Corn-Soybeans, Conventional Tillage,
without Terracing (CB-CV)
Corn-Soybeans, Chisel Plowing, with-
out Terracing (CB-CH)
Corn-Soybeans, No-Till Planting, with-
out Terracing (CB-NT)
Corn-Soybeans, No-Till Planting, with
Terracing (CB-NTT)
Corn-Soybeans Wheat-Hay, Conventional
Tillage for Corn only, without Terrac-
ing (CBWH)
Corn- Soybeans Wheat-Hay, No-Till
Planting, without Terracing (CBWH-NT)
Uplands
Tons/
Acre

26.5

18.9

12.0

8.5

7.0

27.2

15.2

11.4

8.1

4.3


2.7
Farm

Rank

10

9

7

5

3

11

8

6

4

2


1
Ridge
Tons/
Acre

9.1

6.5

4.1

3.0

2.4

9.4

5.2

3.9

2.8

1.5


0.9
Farm

Rank

10

9

7

5

3

11

8

6

4

2


1
Lowlands
Tons/
Acre

3.4

2.4

1.6

1.1

0.9

3.5

2.0

1.5

1.0

0.5


0.4
Farm

Rank

10

9

7

5

3

11

8

6

4

2


1
     Notes:  Soil Loss  = Gross  Erosion
             Highest Rank,  1  =  Minimum Soil Loss

-------
rotation with no tillage  (CBWH-NT).  These soil loss figures refer to gross
erosion rates (before application of delivery ratios).  They  are proportional,
but not directly applicable, to assessment of receiving water impacts.

     For the ridge farm the practice which maximizes annual net  revenue
($26,100) results in annual soil loss of 5.2 tons per acre.  Soil loss on
the ridge farm ranges from 9.4 tons per acre for conventional tillage on the
corn-soybean rotation (CB-CV) down to 0.9 tons per acre for the  no tillage
corn-soybean-wheat-hay rotation  (CBWH-NT).

     For the lowlands farm the farm practice which maximizes annual net
revenue  ($24,600) has an annual soil loss of two tons per acre.  Soil losses
on the lowlands farm range from 3.5 tons per acre for the CC-CV  and CB-CV
practices down to 0.4 tons per acre for the CBWH-NT farm practice.

     Rankings of the farm practices with respect to suspended solids, nitro-
gen, and phosphorus concentrations in the river are shown in Table 6.  The
farm practices and their net revenues can be compared with these pollutant
load contributions in the same manner as illustrated above for soil loss.

     As discussed in Section 5, in addition to soil loss, six variables
related to water quality were analyzed for the three farms and  the 11 farm
practices.  The results, together with net revenues, are displayed as three
sets of bar graphs (Figures 5, 6, and 7).  A complete listing of the water
quality impacts is presented in Appendix D.  The bar graphs are  constructed
so that increasing pollutant  loads or concentrations are shown  by higher
vertical lengths of the bar; for net revenue vertical length increases with
higher returns.  The six water quality components displayed and  the dimen-
sions used to quantify them are

      • Impoundment sedimentation  (kg/m2)1

      • River nitrogen  (g/m3)

      • River phosphorus  (g/m3)
      • River light extinction coefficient  (m"1)

      • Impoundment light extinction coefficient  (m"1)
                                      Q
      • Impoundment biomass  (g chl-a/nr)

     The tables and graphs described above illustrate the types  of informa-
tion produced by the proposed methodology for the case  in which  government
policies are the same as at present.  We emphasize that the  11  selected
farm practices form an incomplete  set of alternatives actually  available
to a farmer; there are many others.  There are also  interesting options
that do not use synthetic biocides and/or fertilizers.  The  body of informa-
tion currently available from Indiana sources is not yet adequate to estimate
costs for these options.  Nevertheless,  estimates are becoming  available
from other sources because of the  increasing use of  such techniques among
large-scale farmers concerned about the  risks of  synthetic biocides.   If
    = kilograms; g = grams; m = meters; yr = years.


                                     37

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OJ
oo
NET REVENUE
or
                                                  NET REVENUE (K £)    20
              RIVER NITROGEN
              (g/m'l
              RIVER PHOSPHORUS
          1
           nl
                                        *
              RIVER LIGHT EXTINCTION COEFFICIENT
               -
                                                                      10  .
                                                  SOIL LOSS ( kg/m2ol
                                                  •oterthod- yr)
                                    SEDMENTATION               .00

                                                                                                        0
                                                                                   MPOUNOMENT LIGHT
                                                                                   EXTINCTION COEFFICIENT   3
                                                                                   (m-l)                 2
                                                                                                         I
                                                                                                        0
                                                                                                      .013
                                                                                   IMPOUNDMENT BIOMASS
                                                                                   (g Chlorophyll- A/m»)  -010
                                                                                                     .005
                                                                                                                      o L
             FIGURE  5:   COMPARISON  OF
                 PRACTICES  — LOWLANDS
                                              FIGURE 6:   COMPARISON  OF
                                                     PRACTICES  —  RIDGE
                                                                                                 FIGURE 7:    COMPARISON OF
                                                                                                        PRACTICES —  UPLANDS

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        TABLE 6:  IMPACTS OF FARM PRACTICES ON AVERAGE ANNUAL CONCENTRATIONS  OF  SUSPENDED  SOLIDS,  NITROGEN,
                                        AND PHOSPHORUS IN THE RIVER

Farm Practice
Continuous Corn, Conven-
tional Till., without Ter-
racing (CC-CV)
Continuous Corn, Conven-
tional Till . , with Terrac-
ing (CC-CVT)
Continuous Corn, Chisel
Plow. , without Terracing
(CC-CH)
Continuous Corn, Chisel
Plow., with Terracing (CC-CHT)
Continuous Corn, No-Till
Plant, without Terracing
(CC-NT)
Corn-Soybean , Conventional
Till, without Terracing
(CB-CV)
Corn-Soybean, Chisel Plow.,
without Terracing (CB-CH)
Corn-Soybean, No-Till Plant.,.
without Terracing (CB-NT)
Corn-Soybean, No. Till.
Plant, with Terracing (CB-NTT)
Corn-Soybean-Wheat-Hay, Con-
ventional Till, for Corn onlji
without Terracing (CBWH)
Corn-Soybean-Wheat-Hay, No-
Till Plant. , without Terrac-
ing (CBWH-NT)
Uplands Farm
SS
kg/m R
3.39 10
2.44 9
1.59 7
1.15 5
.94 3
3.47 11
1.98 8
L.51 6
1.09 4
.60 2
.39 1
N
g/m R
12.6 9
11.5 6
12.6 9
11.5 6
15.6 11
9.5 3
9.5 3
10.5 6
9.9 5
6.5 1
6.8 2
P
g/m R
.09 4
.08 2
.10 8
.09 4
.15 11
.09 4
.09 4
.13 10
.12 9
.07 1
.08 2
Ridge Farm
SS
kg/m R
1.11 10
.81 9
.54 7
.39 5
.33 3
1.13 11
.66 8
.51 6
.37 4
.21 2
.14 1
N
g/m R
18.5 9
17.1 7
18.5 9
17.1 7
22.0 11
13.1 3
13.1 3
14.7 6
14.0 5
8.7 1
8.7 1
P
g/m R
.14 8
.12 3
.13 5
.12 3
.16 11
.14 8
.13 5
.14 8
.13 5
.09 2
.08 1
Lowlands Farm
SS
kg/m R
.50 10
.36 9
.23 7
.17 5
.14 3
.51 11
.29 8
.22 6
.16 4
.09 2
.06 1
N
g/m R
11.1 9
10.2 7
11.1 9
10.2 7
16.3 11
7.8 3
7.8 3
9.7 6
9.2 5
5.6 1
5.8 2
P
g/m R
.19 6
.17 3
.19 6
.18 4
.21 11
.19 6
.18 4
.20 10
.19 6
.14 1
.15 2
OJ
10
       Notes:  SS = Suspended  Solids;  N = Nitrogen;  P  = Phosphorus;  R =  Rank

-------
 Oelhafs  figures (Oelhaf, 1976) are accepted, that cost of farming without
 the use of synthetic chemicals is within 10 to 15 percent of the cost.

     Options for which no illustrative calculations were made include: a ful-
 ler use of year-round rotations; integrated pest management; and integrated
 livestock and cropping operations.  Some of these options may contribute to
 increased economic and environmental stability.  We are convinced that it is
 important to evaluate rotation alternatives (and this includes the CBWH farm
 practices) and at the same time analyze the role of livestock in the farm
 unit.

 INTERPRETATION OF WATER QUALITY IMPACTS

     As shown in Figures 5, 6, and 7, the water quality impacts of agricul-
 tural practices vary with field/soil type, water body (river versus impound-
 ment) , and specific pollutant.  Use of soil loss alone as the criterion for
 farm practice evaluations can lead to erroneous conclusions because of the
 importance of various dissolved components in the water and the interactive
 effects of different processes (e.g., decay, adsorption/desorption, sedi-
 mentation).  For illustrative purposes, Table 7 lists the relative impacts of
 two farm practices on water quality components in the river and impoundment
 for each soil type.  Relative impacts are measured as the ratio of the impact
   TABLE 7:  IMPACTS OF THE MOST EROSIVE PRACTICE (CB-CV) RELATIVE TO THE
        LEAST EROSIVE (CBWH-NT) ON VARIOUS WATER QUALITY COMPONENTS

                                                   Loading or Concentration
            Component*                  Location    Ratio   (CB-CV)/(CBWH-NT)
                                                          S_oil_Type_

Surface Runoff
Gross Erosion
Suspended Solids Concentration
Suspended Solids Concentration
Sedimentation Rate
Dissolved Nitrogen Concentration
Dissolved Nitrogen Concentration
Dissolved Phosphorus Concentation
Particulate Phosphorus Concentration
Total Phosphorus Concentration
Total Phosphorus Concentration
Dissolved Color Concentration
Dissolved Color Concentration
Light Extinction Coefficient
Light Extinction Coefficient
Light Extinction Coefficient**
Chlorophyll-a Concentration**

Watershed
Watershed
River
Impoundment
Impoundment
River
Impoundment
River
River
River
Impoundment
River
Impoundment
River
Impoundment
Impoundment
Impoundment
Lowland
1.25
10.00
9.22
8.40
9.24
1.35
1.22
.81
7.80
1.28
.88
.87
.87
4.58
1.65
1.25
.90
Ridge
4.92
10.00
8.20
6.31
8.30
1.50
1.26
.64
4.29
1.61
.80
1.67
1.67
7.88
4.77
2.00
.80
Upland
1.76
10.00
8.92
7.80
8.97
1.39
1.22
.49
4.20
1.15
.29
.97
.97
8.56
5.93
3.63
.25
 *Annual averages unless otherwise noted.
**Summer averages.
                                     40

-------
on water quality of the most erosive farm practice  (CB-CV) to that of the
least erosive practice  (CBWH-NT).

     For any given soil type the Universal Soil Loss Equation predicts a ten-
fold difference in the gross erosion rates between the two farm practices.
The effects on gross erosion are, however, attenuated by the selective ero-
sion and transport of finer sediment fractions.  Therefore, the ratio of
suspended solids concentrations for the three soil types range from 8.2 to
9.2 in the river and from 6.3 to 8.4 in the impoundment.

     Effects of reducing soil erosion are further attenuated in the case of
river particulate phosphorus concentrations, and the ratios range from 4.2 to
7.8.  River dissolved phosphorus concentrations are actually lower in the
more erosive case, as indicated by ratios less than 1.0 in Table 7.  This
result is attributed to:

     1) snowmelt, which leaches dissolved phosphorus from crop residues
        on the soil surface in the no-till case; and

     2) enrichment of surface soil phosphorus levels caused by the
        shallower tillage and fertilizer incorporation depths charac-
        teristic of the no-till case.

Increases in dissolved phosphorus produced by the CBWH-NT farm practice par-
tially offset the particulate phosphorus decreases resulting from that prac-
tice.  The net result is a 1.2- to 1.6-fold difference in river total
phosphorus concentrations, despite a ten-fold difference in gross erosion
rates.  In the outflow of the impoundment the less erosive farm practice
(CBWH-NT) results in higher phosphorus concentrations than the more erosive
one (CB-CV).  This reversal of effect is attributed to increased impoundment
phosphorus trapping efficiency due to higher sedimentation rate.  This effect
is particularly evident in the relatively steep and phosphorus-deficient up-
land soils.

     Variations in dissolved color also do not follow those of soil loss.
Color differences are attributed to differences in 1)  runoff, and 2)  enriched
levels of organic matter in the surface soil, as influenced by tillage depths.

     Light extinction coefficients are inversely related to water trans-
parencies and are influenced by turbidity (suspended solids), dissolved color,
and in summer algal growth.  Variations in suspended solids concentrations
are chiefly responsible for the 4.6- to 8.6-fold higher river extinction
coefficient values resulting from the more erosive practice.   Because of
selective trapping of coarse suspended solids and color decay within the
impoundment, ratios of annual average impoundment extinction coefficients are
reduced to a range of 1.7 to 5.9 for the various soil types.   With the algal
component included, summer extinction coefficient ratios are further reduced
to the 1.2 to 3.6 range.

     Use of the less erosive practice results in higher chlorophyll-a concen-
trations in the impoundment, ratios ranging from .25 to .90.   This is attri-
buted to 1)  higher phosphorus concentrations in the impoundment  (as discussed
                                      41

-------
above), and 2) the reduced effect of light-limitation on algal growth which
results when turbidity  (suspended solids concentration) is lowered.  In the
extreme — the upland case — implementation of the least erosive practice
causes a ten-fold reduction in soil loss, but a four-fold increase in chloro-
phyll-a concentration.  Chlorophyll-a increases in the other soil types are
less significant, with ratios ranging from  .8 to  .9.

     These results indicate a possible conflict between the water quality
management goals of controlling sedimentation and of eutrophication using
the types of farm practices evaluated here.  Taking into consideration fish
production, higher chlorophyll-a levels could, however, be considered bene-
ficial under certain conditions.  Such conditions might include 1) relatively
shallow impoundments without extensive stratification; 2) chlorophyll-a
concentrations sufficiently low so that occasional major fluctuations in
dissolved oxygen  (due to algal die-offs and/or respiration during cloudy
periods) do not create lethal conditions; and 3) commercial or recreational
objectives that emphasize quantity rather than quality or species of fish
(i.e., "trash fish" are acceptable).  Under these conditions if a model user
were to rank fish production as a higher priority than water quality, there
would be no conflict.  Water quality features that are negatively impacted
by algal production — for example, transparency, taste, odor, or in a strati-
fied impoundment dissolved oxygen concentrations in bottom waters — would be
secondary considerations.  Additional data and analyses are needed to provide
an adequate basis for interpreting the chlorophyll-a predictions from a bene-
fit point of view.  Interpretations would be facilitated by expanding the
impoundment water quality model to permit direct estimation of impoundment
dissolved oxygen concentrations as influenced by both external (watershed)
and internal (photosynthetic) sources of oxygen demand.

     With the possible exceptions of phosphorus and eutrophication, control
of soil erosion produces beneficial effects on water quality.  Nevertheless,
as demonstrated above, the relative magnitudes of these effects are consider-
ably smaller than indicated by relative soil loss.  In addition, effects on
nitrogen concentrations are governed by farm nitrogen budgets rather than
soil loss.

     The importance of one pollutant compared to another may also shift from
watershed to watershed and hence influence the selection of those water
quality components of primary importance to the evaluation of the BMP's;
that is, the different pollutants should be ranked on the basis of the sever-
ity of local water quality issues.  In assessing BMP's it seems reasonable
to first compare farm practices and their net revenues with respect to the
primary pollutants and then incorporate the other pollutants into the analy-
sis.

     To illustrate the linking and application of the farm and water quality
models, soil loss and nitrogen are used as basic measures of water quality
impact in the following discussion.  More detailed discussions and interpreta-
tions of the water quality impacts of the practices and soil types are
included in Appendix D.
                                      42

-------
FARM PRACTICES AND FUTURE POLICIES

     The purpose of linking the farm and water quality models is to evaluate
the effects of proposed government policies concerned with agricultural prac-
tices on farm income, water pollution loadings, and water quality.  For
illustrative purposes we consider the following policies.

Conservation Practice Subsidies or Requirements

     First, let us consider erosion control subsidies for structural improve-
ments.  Terraces are an important soil-saving option.  Their total annual
cost for our farm of 250 acres is estimated at $6,460, nearly all of which
represents construction costs.2  This is incorporated in Table 4 as a cost
totally borne by the farmer, and as a consequence terracing alternatives look
less attractive than other alternatives.

     A 50 percent terracing subsidy, however, brings the net revenues of the
continuous corn chisel plow alternatives on terraced land  (CC-CHT) more in
line with the highest non-subsidized practice of corn-soybean chisel plow
 (CB-CH) on non-terraced land.  Although the corn-soybean chisel plow practice
on terraced land  (CB-CHT) was not computed, that alternative would be slightly
more favorable than continuous corn with chisel plowing on terraced land
 (CC-CHT) and would presumably be selected with the 50 percent subsidy.  Such
a subsidy amounts to $3,230 per 250 acres or about $13 an acre.  Soil loss
reduction and cost per unit improvement are3

                             Soil Loss        Cost Per Ton of
                             Reduction     Reduction in Soil Loss
                Upland:       7  tons/acre            $  1.90
                Ridge:        2  tons/acre            $  6.50
                Lowland:      1  ton/acre             $13.00

Prohibition of Certain Cultivation Practices

      The second class  of  policies  —  prohibition  of  cettain  tillage practices
 such as  conventional plowing —  would have  no apparent  economic  impact  on  the
 farms analyzed here, but  could reduce soil  loss.   This  assumes,  of course,
 equal access  by a farmer  to moldboard and chisel  plows.   Table 4 directly
 indicated the cost impact on the farmer  of  any required shift in crop prac-
 tice by  comparing the  forbidden  maximum  revenue alternative  to the permitted
 maximum  revenue alternative.

 Comparison of Terracing and Prohibition  of  Tilling Practices

      We  may also compare  the two policies for reducing  soil  loss:  the  $3,230
 subsidy  per farm; and  the prohibition of certain  tillage  practices.   For
 example, prohibiting moldboard plowing in favor of chisel plowing for
 2See Appendix A,  Table A-l  for derivation of  terrace  cost.
 3The soil loss estimates shown  in Table 5  are rounded to the nearest ton in
 this and subsequent examples.
                                      43

-------
continuous corn  (CC-CH) or corn-soybean rotations on non-terraced land
 (CB-CH) reduces  the soil loss as follows:

                           Continuous Corn    Corn-Soybean Rotation

               Upland:      15 tons/acre         12 tons/acre
               Ridge:        5 tons/acre          4 tons/acre
               Lowland:      2 tons/acre          1 ton/acre

The substitution of the plowing implements could be accomplished for less cost
than the terrace subsidy, and major improvements in soil loss could thus be
achieved on the upland farm.  If the farmer were subsidized for the acquisi-
tion of a $2,150 chisel plow, the cost would be no more than $350 per year;
this is the yearly fixed cost for the implement.  If the farmer liquidated
a moldboard plow as part of a farm implement subsidy package, the cost of
the subsidy program could be reduced.  From another view, if we assume that
the value of each ton of soil retained by terracing is judged to be worth the
50 percent subsidy involved (e.g., on the uplands farm this amounts to $1.90
per ton subsidy), then prohibition of moldboard plowing in favor of chisel
plowing on the upland farm is worth approximately $25 per acre for continuous
corn and the corn-soybean rotation.  This value is about double the $13 per
acre value implied by the 50 percent terrace subsidy.

Gross Soil Loss Restrictions

     Gross soil loss restrictions are sometimes suggested as watershed plan-
ning goals, if not absolute prohibitions.  There are numerous ways to apply
such restrictions, but for the purposes of this exposition we consider them
to apply over each acre of a watershed.  Such an interpretation maximizes
their impact on costs and on erosion.

     Consider, for example, a restriction on gross soil loss of four tons/acre
maximum.  This implies the following mandated shifts in cropping activities
to comply with four tons/acre soil loss.

     1. For the upland farm the practice with highest net revenue that meets
the soil loss criterion is the corn-soybean-wheat-hay rotation with no til-
lage (CBWH-NT); additional herbicides are used in the spring to kill the
remaining sod before planting corn.  (Note that we are considering soil loss
as the primary problem; on other grounds use of biocides would probably be
rejected in favor of mechanical cultivation which would, of course, increase
soil loss to four tons/acre, a bit above the loss expected with the CBWH-NT
farm practice).  Net revenue decline is

                        CB-CH   = $13,700
                        CBWH-NT = $12,800

                        Decline = $   900 for 250 acres

Reduction in soil loss is about (15 tons/acre for the CB-CH practice - 3 tons/
acre for the CBWH-NT practice)  = 12 tons/acre.
                                     44

-------
     2. For  ridge soils costs to the farmer are somewhat greater, and soil
loss reductions considerably smaller.  The shift is from corn-soybean with
chisel plowing (CB-CH) to corn-soybean with no tillage (CB-NT):

                        CB-CH   = $26,100
                        CB-NT   = $25,100

                        Decline = $ 1,000 for 250 acres

Reduction in soil loss is only one ton/acre.

     3. For the lowlands farm no change from the net revenue maximizing farm
practice (CB-CH)  would be necessary to meet gross soil loss restrictions of
four tons/acre.

Gross Soil Loss Taxes

     Gross soil loss taxes are a fourth type of policy of interest in control-
ling water pollution.  For illustrative purposes a tax of 40 cents per ton
on soil losses is assumed, and economic impacts are measured.

     1. For the uplands farm corn-soybean with chisel plowing  (CB-CH) is the
net revenue maximizer without tax; soil loss is 15 tons/acre or 3,750 tons/
year for the farm.  Tax is $1,500, so the new net revenue is  (13,700 - 1,500)
= $12,000.  The CBWH-NT practice has a soil loss of three tons/acre or 250
tons/year, so tax is $300 and new net revenue is  ($12,800 - $300) = $12,500.
Therefore, net revenues are greater, and the CBWH-NT practice would be chosen.

     2. For the ridge farm the impact of a soil loss tax on the revenues from
the 11 practices is shown in Table 8.  Minor changes in ranking of the net
revenues occur as a result of the soil loss tax.  However, the advantage of
chisel over conventional plowing in terms of dollars net revenue is increased.

              TABLE 8:  IMPACTS OF SOIL LOSS TAX  (1977 DOLLARS)
                                (RIDGE FARM)
Farm
Practice
CC-CV
CC-CVT
CC-CH
CC-CHT
CC-NT
CB-CV
CB-CH
CB-NT
CB-NTT
CBWH
CBWH-NT
Net Revenue Soil Loss
$ Rank (tons/acre)
23,600
20,300
24,100
20,900
20,100
25,800
26,100
25,100
21,500
20,800
21,100
5
10
4
8
11
2
1
3
6
9
7
9
7
4
3
2
9
5
4
3
1
1
Revenue After
Tax Tax
($.40/ton) $ Rank
900
700
400
300
200
900
500
400
300
100
100
22,700
19,600
23,700
20,600
19,900
24,900
25,600
24,700
21,200
20,700
21,100
5
11
4
9
10
2
1
3
6
8
7
                                      45

-------
     3. For the lowlands farm the taxes and impacts would be small for a soil
loss tax because there is little erosion potential with any of the farm prac-
tices.

Fertilizer Limitations or Taxes

     A fifth policy type considers a fertilizer tax to reduce over-application
of fertilizer — especially nitrogen.  The rationale behind such a tax would
be as follows.  Because of the small slope of the fertilizer response curve
in the region of interest (where farmers now operate), a tax can encourage
less fertilizer use with modest declines in crop yield and even smaller re-
ductions in net revenue.  However, the effects of reduced nitrogen applica-
tions are magnified as beneficial impacts on water quality because of the
non-linear nature of the water body response to nitrogen.  For example, see
Figure 8.  To evaluate the implications of a fertilizer tax policy, two
approaches are illustrated.  In the first approach a relatively high tax on
nitrogen fertilizer is investigated to determine how changes in farm prac-
tices might be induced and how water quality would be affected.  In the sec-
ond approach we show that relatively large reductions in nitrogen use can be
attained with small reductions in yield.  A fertilizer tax might be used to
obtain this result without changing the agricultural practice desired by the
farmer.

     Nitrogen fertilizer is first assumed to be heavily taxed at $0.07 per
pound, representing a price increase of about 50 percent over the price used
in the reference cases developed in Appendix A.  This tax reduces net reve-
nues by a maximum of $3,400 (on the ridge farm) for the farm practice using
the most nitrogen (CC-NT)  and by about $900 for the least nitrogen-dependent
practices (corn-soybean-wheat-hay rotations).  The corn-soybean chisel plow
farm practice (CB-CH) is still the highest ranking net revenue practice, but
both of the corn-soybean-wheat-hay alternatives have moved up in the ranking,
as shown in Table 9.  We can estimate water quality implications from data
presented earlier in Table 6 on river nitrogen concentrations from the
various farm practices.  For example, if a sufficiently high fertilizer tax
could be imposed so that net revenues for the corn-soybean-wheat-hay no-till
farm practice (CBWH-NT) were equal to those for the corn-soybean chisel plow
(CB-CH), river nitrogen would be reduced 28 percent for the uplands farm,
34 percent for the ridge farm, and 26 percent for the lowlands farm.  This
requires a fertilizer tax of $0.13 per pound (or a 100 percent increase in
the price of nitrogen to the farmer) for the uplands farm.  For the ridge
farm the tax required is $0.54 per pound  and  for the lowlands farm, $0.74
per pound, representing nitrogen price increases to the farmer of 415 percent
and 570 percent respectively.

     For the second illustrative case the use of nitrogen is somewhat reduced,
and the farmer continues to select the same agricultural practice as in the
reference case.   We have used corn-nitrogen response functions to estimate
the yields for different levels of nitrogen application (see Appendix F,
unattached), and to illustrate the impacts, we have considered one of the
farm practices that is a heavy user of nitrogen — the continuous corn with
chisel plowing (CC-CH).  In the Black Creek area on ridge soils, nitrogen
                                      46

-------
FIGURE  8: EFFECTS OF FERTILIZATION RATE ON LOW YIELD AND RIVER NITROGEN

      .„_                  CONCENTRATIONS
120
w.
(J
^ 100
QQ
"5)
> 80
c
V.
O
o
60
40(
—
CORN YIELC
vs
FERTILIZATI
(RIDGE FAR
)
ON
M)

X
/
/
/


*
/

)
/
/


/




'





|/






X







x^








^^*








< —









•i ••



















40 80 120 160 200
    E
    QL
    Q.

    fc  16
    0>
    I

    o
    c
    0)
    o

    o
    o

    c
    0)
    OJ

                  Fertilization-Lbs. Nitrogen per Acre
12
 8
    ±:   4
     RIVER NITROGEN
          vs
     FERTILIZATION

     (RIDGE FARM)
                             NOTE:

                             ASSUMES 50per cent of EXCESS

                             NITROGEN IS DENITRIFIED and

                             0.25m/yr TOTAL FLOW
         0
          40       80      120      160      200

           Fertilization-Lbs. Nitrogen per Acre
                                 47

-------
 application is 160 pounds per acre, resulting in corn yields of 130 bushels
 per acre.  Reduction in nitrogen application of 13 percent is selected and
 thus reduces corn yields about 2.5 percent and gross revenue by the same
 amount.

 TABLE 9:  NET REVENUE — 1977 DOLLARS (FERTILIZER TAX* IMPOSED ON NITROGEN)
Farm Practice
Continuous Corn,
Conventional Tillage, with-
out Terracing (CC-CV)
Continuous Corn ,
Conventional Tillage, with
Terracing (CC-CVT)
Continuous Corn,
Chisel Plowing, without
Terracing (CC-CH)
Continuous Corn,
Chisel Plowing, with
Terracing (CC-CHT)
Continuous Corn,
No-Till Planting, with-
out Terracing (CC-NT)
Corn-Soybean ,
Conventional Tillage, with-
out Terracing (CB-CV)
Corn-Soybean ,
Chisel Plowing, without
Terracing (CB-CH)
Corn-Soybean ,
No-Till Planting, without
Terracing (CB-NT)
Corn-Soybean ,
No-Till Planting, with
Terracing (CB-NTT)
Corn-Soybean-Wheat-Hay ,
Conventional Tillage for
Corn only, without Terracing
(CBWH)
Corn-Soybean-Wheat-Hay, No
Till Planting, without
Terracing (CBWH-NT)
*Tax on nitrogen is assumed to
Uplands
$
10,500
7,200
11,100
7,800
4,300
12,400
12,600
11,000
7,400
11,700
12,100
Farm
Rank
7
10
5
8
11
2
1
6
9
4
3
be 7 cents per
Ridge Farm
$ Rank
20,600 5
17,300 10
21,100 4
17,900 9
16,700 11
21,600 3
24,800 1
23,600 2
19,900 7
19,800 8
20,200 6
pound .
Lowlands
$
19,300
16,000
19,900
16,600
3,200
23,100
23,400
15,100
11,500
17,200
16,800

Farm
Rank
4
8
3
7
11
2
1
9
10
5
6

The resulting impact on net revenue is a four percent reduction.  If farmers
responded to small changes in fertilizer prices, they would lower their operat-
ing costs by an amount equal to the decline in revenue caused by a fertilizer
tax.  In this illustration the 13 percent decrease desired from the use of
nitrogen would be accomplished by a fertilizer tax of about $0.04 to $0.05 per
pound.  River nitrogen concentration is reduced by approximately 20 percent


                                      48

-------
(i.e., 18.5 g/m3 to 14.4 g/m3)  by the lowered levels of fertilizer use on the
ridge farm.  This level of pollutant reduction is explained by Figure 8.  It
is seen that the corn-nitrogen response curve is relatively flat in the range
of interest (i.e., large reductions in nitrogen application result in small
reductions in yield).   Nevertheless, as the figure shows, the percent reduc-
tion in river nitrogen is greater than the reduction in nitrogen applied to
the crops.

Manure/Legume Subsidies or Restrictions

     The final type of policy evaluation considered is a subsidy for construc-
tion of manure storage and handling facilities, or for growing leguminous
cover crops to protect the soil and provide crop nitrogen.  Because we have
not included livestock activities in the methodology developed to date, we
consider here only a hay crop subsidy that affects the corn-soybean-wheat-hay
rotations.  The objective might be to encourage use of such a rotation to
conserve soil, nitrogen, and energy.

     If the lowlands farm is considered, net revenues for maximum net
return — corn-soybean with chisel plowing (CB-CH) — is $24,600.  Net reve-
nue for the alternative that we wish to encourage — corn-soybean-wheat-hay
(CBWH) — is $18,00 in the reference case.   With a expected yield of four
tons per acre and one-quarter of the farm in hay  (62.5 acres), the incremen-
tal price needed to bring the CBWH practice up to the net revenue level  for
the CB-CH practice is  ($24,600 - $18,100) T  (4 x 62.5) = $26 per ton.  This
is not impossible, especially if an integrated livestock operation is con-
sidered.  However, a subsidy in that amount  ($26 per ton or about $100 per
acre) could foster the switch to the CBWH practice at current prices for hay
of $60 per ton.

Alternative Futures

     One alternative future is a continuation of the trends toward a highly
concentrated, factory-like food/fiber production system, characterized by
trends listed in Section  1.  Aspects of other possible futures evolving  out
of past and current trends and new  forces might include  elements from the
following  list.

      1) Stabilization  of  farm sizes and potential reduction  in size
        of the  largest units
      2) Reversal of the trend toward absentee  ownership

      3) Increased  labor inputs as energy  costs increase

      4) Regional and local implement manufacturing  operations with
        focus on the needs of the part-time small farmer

      5) Crop price stabilization  through international establishment
        of grain  reserves

      6) Some reversion to polyculture  for economic  and ecologic
        reasons as energy costs  increase,  to the  extent that the
        environmental  problems of synthetic  biocides become a problem
                                      49

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      7) More use of manure, rotations, and.composted urban organics
        for fertilization and biological control for pest management

      8) Increasing integration of livestock activities with feed/food
        farming as energy costs force more on-farm use of manure as
        a feed, fertilizer, and energy (methane) source, and as the
        pollution costs of feedlot operations are passed back to the
        feedlot operator.

      9) State/federal assistance to persons desiring to farm by direct
        subsidies  (soft loans) and innovative land use controls (e.g.,
        purchase of development rights by the state)
    10) Adjustments in the organization of marketing and distribution
        systems to meet the needs of smaller farm operators

    11) Consumer and farmer reaction to costs

      In order to carry out evaluations that include these kinds of shifts in
agriculture, a more complete and complex analysis than was possible in this
study is required.  However, data exist  to explore some of these items and
could be incorporated in an automated farm model.

      In this study we can illustrate how a properly structured farm model
would be used to evaluate farm practices and water quality impacts in a
future economic setting.  The example concerns increased energy costs, but
does  not include changes in labor inputs as suggested in the above list,
item  3.

      Many of the inputs to farm production involve the use of energy derived
from  oil and natural gas.  Farm inputs requiring substantial amounts of
energy include fuels used on the farm and energy that is consumed or embodied
in the production of fertilizers and biocides.  For example, in addition to
diesel and gasoline fuels for tractors and combines, corn drying operations
consume about 15,000 Btu per bushel for every ten points of moisture reduc-
tion.  Nitrogen fertilizer requires 20,000 to 25,000 Btu for every pound that
is manufactured, while production of biocides requires anywhere from 40,000
to 195,000 Btu per pound depending on their particular formulation.**

     Prices paid by farmers for fuels and chemicals will continue to rise
because of diminishing oil and gas reserves and possibly because of the
actions of cartels to create higher oil prices in the long run.  It is also
likely that decontrol of natural gas prices will be implemented in the next
five to ten years.  It seems reasonable to assume that equal prices will
eventually be established based on Btu content.  Farm practices that are
more heavily dependent on mechanization and use of chemicals will be impacted
most severely compared to the less energy-dependent cultivation practices.

     We have postulated an economic future for 1985.  Prices for tractor and
combine fuel,  grain drying operations, and the various chemicals bought by
the farmer will be substantially higher.   In the case illustrated here we
^Personal communication, D. Pimental,- Cornell University, November, 17, 1977.
                                      50

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assumed 1985 energy prices will be approximately double the 1977 prices,5
while prices for other inputs remain constant.  This projected increase is
stated in constant 1977 dollars and therefore does not include inflationary
trends.

     Maintaining the same 11 farm practices previously described results  in
increased cost of farm operations ranging from $10,000 to $30,000 annually,
depending on the practice. This range corresponds to a 30 tb 65 percent in-
crease over 1977 costs. Net returns are, of course, drastically affected. Need-
less to say, profitability depends on revenues as well as costs.  We have
not, however, attempted to project prices received by the farmer for corn,
soybeans, wheat, and hay; even if this had been done, it is possible that
some of the farm practices would no longer appear to be financially viable.
Since we are interested in the potential impacts of farm practices on water
quality as induced by profitability considerations,  it is sufficient to
evaluate changes in farm costs without attempting to adjust gross revenues.
A more complex projection would consider substitution, technological change,
and farm scale change effects that are beyond the scope of the present effort.

     Table 10 shows the impacts from the future energy prices.  On all three
farms the corn-soybean-wheat-hay rotations indicate their lesser dependency
on energy by an upward shift in their net revenue rankings compared to the
reference cases with 1977 energy prices.  The impacts are most dramatic on
the uplands farm.  The CBWH-NT and CBWH net revenues are ranked one and two
respectively, compared to a 1977 ranking of four and six.  Moreover, the
annual soil loss with these two farming practices is four tons per acre or
less, whereas the highest net revenue practice in 1977  (CB-CH) has a soil
loss of 15 tons per acre on the uplands farm.
5Energy Resources, Inc., "Data Resources Outlook for the U.S. Energy Sector:
 Control Case," Energy Review. Summer, 1977.
                                      51

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                     TABLE 10:  EFFECT OF FUTURE ENERGY PRICES  (CONSTANT 1977 DOLLARS)
Uplands
Farm Practice
Continuous Corn, Conventional
Tillage, without Terracing (CC-CV)
Continuous Corn, Conventional
Tillage, with Terracing (CC-CVT)
Continuous Corn, Chisel Plowing,
without Terracing (CC-CH)
Continuous Corn, Chisel Plowing,
with Terracing (CC-CHT)
Continuous Corn, No-Till Plant-
ing, without Terracing (CC-NT)
Corn-Soybeans , Conventional
Tillage, without Terracing
(CB-CV)
Corn-Soybeans, Chisel Plowing,
without Terracing (CB-CH)
Corn-Soybeans, No-Till Plant-
ing, without Terracing (CB-NT)
Corn-Soybeans, No-Till Plant-
ing, with Terracing (CB-NTT)
Corn-Soybeans-Wheat-Hay ,
Conventional Tillage for Corn
only, without Terracing (CBWH)
Corn-Soybeans-Wheat-Hay ,
No-Till Planting, without
Terracing (CBWH-NT)
1985 net
revenue*
$ rank
- 8,100 8
-11,700 10
- 7,400 7
-11,000 9
-19,900 11
300 4
80 3
- 3,200 5
-7,000 6
+ 50 2
+ 2,600 1
1977
net
rev.
rank
4
9
3
8
11
2
1
7
10
6
4
soil
loss
rank
10
9
7
5
3
11
8
6
4
2
1
Ridge
1985 net
revenue*
$ rank
+ 800 7
- 2,800 9
+ 1,500 6
-2,100 8
- 6,000 11
+11,600 2
+11,800 1
+ 9,300 4
- 5,300 10
+ 8,200 5
+10,700 3
1977
net
rev.
rank
5
10
4
8
11
2
1
3
6
9
7
soil
loss
rank
10
9
7
5
3
11
8
6
4
2
1
Lowlands
1985 net
revenue*
$ rank
- 1,500 7
- 5,000 10
800 6
- 4,400 9
-22,500 11
+ 8,800 2
+ 9,000 1
400 5
- 4,200 8
+ 4,900 4
+ 7,600 3
1977
net
rev.
rank
4
6
3
5
11
2
1
9
10
7
8
soil
loss
rank
10
9
7
5
3
11
8
6
4
2
1
Ul
to
      Notes:   Highest soil loss rank,  1 = minimum soil loss.

              Highest revenue rank,  1  = maximum net revenue.

      *Output prices assumed to remain at 1977 level.

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                                    SECTION  7


                          IMPACTS ON DOWNSTREAM USERS
      As discussed in Section 6, the results of combining the farm, watershed,
 and impoundment models and applying them to a case study area show that the
 use of alternative farm practices on different soils has  different water
 quality impacts.  Changes in water quality caused by changing farm practices
 have impacts on downstream water users.   To estimate these impacts, changes
 in water quality must be related to measurements of value to people.  If this
 could be accomplished, the beneficial impacts of alternative agricultural
 practices on downstream users could be compared with the costs (management,
 environmental,  and social, to farmers and others)  of instituting alternative
 farming practices.  The decision maker could then decide if the beneficial
 impacts (benefits) of instituting a particular policy are worth the costs.
 This is,  however,  a difficult step,  especially since we are concerned here
 with more than  one water quality parameter and many downstream users.

      A benefit  estimation study is a major undertaking  in terms of time and
 expense and  has therefore seldom (or never)  been done at the comprehensive
 level desirable for estimating  the impacts of changes in more than six water
 quality variables  on a multiple-use  impoundment.   Table 11 shows  alternative
 methodologies that are appropriate for measuring different water  quality
 benefits.  Depending on the  use of the water and the surrounding  land uses,
 certain impacts are of more  or  less  interest to groups  of  people  concerned
 with water quality.   Therefore,  it is necessary to determine  which groups
 are  likely to derive the  most benefit from which aspects of improved  water
 quality.

      As an example of  the interests  of different groups, let  us assume  that
 the  watershed in which the farmland  is located  drains into  a  small  stream
 used by local sport  fishermen in  the  spring.  Downstream is an impoundment
 created for  the purposes  of water  supply,  flood control, and recreation.
 The  impoundment  is a major recreational and  aesthetic attraction  in the
 region, attracting people  from  surrounding counties  to  swim, boat,  fish, and
 picnic.  Let us also assume that a town uses the reservoir  to supply water
 for  drinking and other purposes.  Some benefit categories of  interest in this
 case  are:  human health, municipal water supply, flood control, ecology,
 recreation, aesthetics, and the local economy.  The methods of benefit esti-
mation vary according to the benefit categories of interest and have been
discussed and evaluated according to the criteria outlined  in Appendix E.
The  following paragraphs briefly indicate possible research approaches  for
each of the above categories.
                                      53

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              TABLE 11:  COMPARISON OF METHODOLOGIES TO MEASURE WATER QUALITY BENEFITS



time
budget
bidding

games
travel
costs

marginal
costs

net factor
income
market
study
non-dollar
measurement
input/out-
put model
alternative



U)
aesthetic

X












ranking





c
recreatio

X




X







ranking






property
values













X






Benefit Categories

I!







medical
costs
ft lost
earnings









-H
commercia
fishing











yield
change
x price








.
municipal
water supp








treatment
sroduction
costs









H ^
industria
water sapp]








treatment
production
costs









•-t --.
«J H
dredging
(navigatior
flood contrc









X











o
•H
o
a














change
in
habitat


cost to

reproduce

local or
regional
economy
















X



     Human Health.  Epidemiological  data must be gathered and analyzed to
relate morbidity and mortality  rates to drinking water nitrate or biocide
levels or both.  Health effects would then be related to their value to
people either by:  1) calculating  a  dollar value for medical costs and lost
earnings for each rate of morbidity  and mortality;  2)  surveying the relevant
population using a bidding game approach to determine aggregate willingness-
to-pay to avoid each level of health effect;  or 3)  a combination of both of
these methodologies.

     Municipal Water Supply.  Variations in treatment cost, including equip-
ment and maintenance costs, must be  estimated for alternative pollutant
(sediment, etc.) levels.

     Flood Control.  Sediment deposition affects frequency and severity of
flooding.  This relationship also  must be specified, and the cost of related
flood damage calculated.

     Ecology.  One possible approach ranks habitat changes that affect growth
of organisms caused by water quality changes.  Diversity is one criterion
used to define this ranking.  Another approach would be to calculate the
cost of reproducing the function that the ecology of the region provides and
that would be altered by water  quality changes.
                                      54

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      Recreation.   Recreation covers both contact activities such as swimming,
 and non-contact activities such as boating.   The travel-cost method is one
 of the accepted methodologies available to construct a demand function
 dependent on alternative levels of water quality,  using data on variations
 in distance traveled to recreation sites as  a surrogate price for the  acti-
 vity.   This method may not be the best choice,  since in one example most of
 the users of this impoundment are local and  do  not travel long distances.

      Another approach,  the bidding game, relies on survey data to indicate
 the highest amount people would be willing to pay  for an improvement in
 water quality.   The bidding can be tied into an appropriate mechanism  such
 as a water bill,  a recreation fee, or  a tax.  Results,  however,  seem depen-
 dent on assumed starting bids.

      In the time  budget approach,  also using a  survey format,  respondents
 describe their  activities and expenses during a certain time period — a
 week,  for example — which are then matched  with certain levels  of environ-
 mental quality.   This information is used to build a demand curve.

      For sport  fishing,  another important recreational  activity,  benefits
 accruing to fishing have been related  to a fish response model.   This  model
 simulates fish  responses in terms  of quantity and  type  to water  quality
 changes.   With  commercial fishing,  benefits  could  be derived by  translating
 the particular  fish population  into a  dollar  measure of changes  in income,
 assuming constant prices.   Sport  fishing variables other than  success  are
 important to  the  recreational  experience.  In might be  possible  to  combine
 the  fish response device with one  of the survey methods described  above  to
 obtain  information on sport fishing benefits.

     Aesthetics.   The aesthetic and visual aspects of the  river or  impound-
 ment water  quality are determined  by attributes such as color, depth percep-
 tion,  the existence  of weeds, etc.

     One  approach would  be  to consider  aesthetics  along with recreation bene-
 fits in  a time-budget or bidding game  survey.   The population  sample sur-
 veyed would then  be  expanded to include  non-recreationists.  Typically, rank-
 ing methods have  been used  to ascertain  the value of  the aesthetic qualities
 of natural resources.  One  difficulty is  that the aesthetic value of a water
body is greatly influenced  by its  surroundings and characteristics other
 than water quality.  A good non-monetary  ranking system used in conjunction
with the  survey methods would be valuable as a reliability check.

     Local Economy.  An  input/output model could be constructed for the
regional  economy  surrounding the impacted water body.   Increased expenditures
generated by recreationists or tourists  (see above) in response to changes
 in water quality could be used in the model to calculate the resulting in-
crease in household income  and local production.

     We have outlined possible elements of a comprehensive benefit estima-
tion methodology.  It is clear that such a study would require significant
time and resources to implement and would present many empirical difficul-
ties.  As an alternative, we would like to present a simplified version that


                                     55

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qualitatively assesses  the direction of benefits resulting from water quality
changes induced by  the  alternative farming practices.   This is considered a
substitute for the  major effort which would be required to implement a quan-
titative benefit  estimation methodology.

     Table 12 indicates which water quality components  impact which benefit
categories.  A minus sign indicates that an increase  in the water quality
measurement has a detrimental effect on the specified benefit group; for
example, an increase in nitrogen concentration in  drinking water is poten-
tially harmful to human health.  A zero indicates  that  an increase in the
parameter  is of no  importance to the benefit category.   For instance, the
same increase in  nitrogen concentration just mentioned  would not impact dredg-
ing operations in  the impoundment.  A water quality measurement increase which
has a positive impact on a benefit category is indicated by a plus sign.
Increasing impoundment biomass, for example, might improve sport fishing,
since more food might increase the available fish  population.

           TABLE 12:   IMPACTS ON BENEFIT CATEGORIES OF WATER QUALITY COMPONENTS*

Benefit
Categories**
human health
(drinking water)
municipal
water supply
flood control
ecology
recreation
sport fishing
contact
non-contact
aesthetics
local economy
Water Quality Components
Impoundment
Sedimentation
(kg/m2)

0
-
-
-

-
o(-)
o(-)
o(-)
-
Impoundment
Sediment
outflow
Concentration
(kg/m3)

-
-M
0
-

-
-
-
-
-
River and
Impoundment
Nitrogen
(g/m3)

-
-
0
-

0
-
0
0
-
River Light
Extinction
Coefficient
(m'1)

-
-
0
-

-
-
-
-
-
Impoundment
Light
Extinction
Coefficient

-
-
0
-

-
-
-
-
-
Impoundment
Biomass
(g chloro-
phyll-a/m3)

-
-
0
-

+(-)
-
-
-
-W
 •The effect on a benefit category of an increase in any parameter is noted as follows:
 detriment - -; no effe.t » 0; benefit - +.
 "See text for explanation of benefit categories.

      There are several cases  in which  the  impact of a water quality change
 on a benefit category is not  totally clear.   These are noted by alternative
 signs in parentheses.  Four such cases are evident in Table 12:
                                       56

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     1) Sedimentation in a municipal water supply is mainly detrimental
        because it causes turbidity, carries chemicals and other toxic
        materials, and, if it occurs in high concentrations, must be
        removed during treatment.  On the other hand, sediment does
        tend to adsorb odor and taste-producing chemicals which might
        otherwise require artificial flocculation (coagulation).  This
        possible benefit is considered to be less important than the
        detriment, and therefore a minus sign is used to show the
        dominant effect.
     2) An increase in impoundment biomass may have a positive effect
        on sport fishing, since it means an increase in food supply
        for fish and hence in fishing success.  With excessive amounts
        of algal growth, however, bottom conditions deteriorate and
        dissolved oxygen levels decrease, causing a decrease in desir-
        able fish species, such as trout, and an increase in trash fish,
        which survive better under such conditions.   This may ultimately
        have a negative impact on sport fishing.  In our case example,
        however, we assume that increasing biomass levels can be viewed
        as beneficial to sport fishing.

     3) The local economy benefit category is dependent on the benefits
        to tourists and recreationists, and therefore the water quality
        impacts observed will be positive or negative according to the
        impacts on the recreation and aesthetic benefit categories.
        Since an increase in biomass has a negative impact on contact
        and non-contact recreation as well as aesthetics, it will most
        probably have a negative impact on the local economy despite
        its generally positive impact on sport fishing.  The opposite
        would be true only if much of the local economy were dependent
        on an influx of fishermen, which we did not assume.
     4) Sedimentation reduces the holding capacity of an impoundment.
        When this effect is slight and the impoundment is large, there
        will be insignificant impacts on contact and non-contact recrea-
        tion and aesthetics — assumed in Table 12.   However, in some
        cases sedimentation could be a very grave problem in an impound-
        ment, causing it to fill in and cease to exist.

It is clear from Table 12 that with the possible exception of the beneficial
impact of higher biomass levels on sport fishing, all categories are either
not influenced or negatively influenced by an increase in any of the water
quality components.

     In order to compare the practices from the downstream users' point of
view  we need to select a base case; this is the case option producing the
highest net revenue (the corn-soybean rotation using chisel plowing), essen-
tially assuming that the farmer is a maximizer of net revenue.   Figures 9,
10, and 11 depict the relative water quality and net revenue impacts (measured
as percentage increases or decreases relative to the base case) of the other
ten practices on the various soil types.
                                     57

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oo
     Fir.tlRF 9:  PKRCEHT CHANGE OF H1W1KST REVENUE FACTOR — I.OWLAND

     REVENUE
      SOL LOSS
                  40
                  0
                 -40
                 -60
      SEDIMENTATION
            40
             0
           -40
           -80
RIVER NITROGEN
(%)        120
            80
            40
             0
           -20
RIVER PHOSPHORUS
(%)         20r
                              1
                                      fy/ff^f*
      RIVER LIGHT EXTINCTION COEFFICIENT
      (%)         60
                  20-
                  0
                 -20
                 -SO-
      IMPOUNDMENT LIGHT EXTINCTION COEFFICIENT
      (%)         20r
                  °F
                 -201
      IMPOUNDMENT BIOMASS
      (%)         20
                                              J	L_J
                                                                                   FIGURE 10:  PERCENT CHANGE OF HIGHEST REVPNIIE FACTOR — RItXiF.
                                                                                 REVENUE (%)
                                                                                 SOIL  LOSS  CM
                                                                                                               7/7,
                      80
                      40
                       0
                      -4O
                      -80

                      40
SEDIMENTATION (%)     0
                      -40
                      -80
                      80
                      4O
RIVER   NITROGEN  (%)  0
                      -40

RIVER   PHOSPHORUS    0 EESfaJ^zra
        (%)           -20

                      40 :
RIVER LIGHT
EXTINCTION  COEFFICIENT 0
        (%)          -40 L
                                                                           IMPOUNDMENT  LIGHT    20
                                                                           EXTINCTION COEFFICIENT   0
                                                                           IMPOUNDMENT  BIOMASS
                                                                                   X%)
II

-------
   FIGURE 11.   PERCENT CHANGE OF HIGH-
   EST REVENUE FACTOR—UPLANDS
  REVENUE CM
  SOIL LOSS (%)
  SEDIMENTATION (%)
  RIVER NITROGEN OW
  RIVER
      PHOSPHORUS
       nu
                                          The downstream benefits of alternative
                                          farming practices can be qualitatively
                                          compared by mapping the quantitative
                                          practices and water quality relation-
                                          ships depicted on Figures 9, 10, and
                                          11 onto the qualitative water quality
                                          benefit relationships presented in
                                          Table 12.  Results are summarized in
                                          Table 13 for a comparison of the
                                          corn-bean-wheat-hay rotation with the
                                          assumed base case (corn-soybean rota-
                                          tion with chisel tillage).  The rows
                                          in Table 13 correspond to different
                                          benefit categories,  and the columns
                                          to different water quality components.
                                          As in Table 12,  a positive sign indi-
                                          cates that switching from the base
                                          case to the compared practice pro-
                                          duced a beneficial impact on the
                                          corresponding benefit category.   The
                                          percentage changes in the  various
                                          water quality components,  necessarily
                                          considered in evaluating the results,
                                          are  also listed  in Table 13.   The
                                          only negative impact of switching to
                                          the  CBWH rotation is related to  the
                                          impoundment biomass  column —  namely,
                                          the  impact on sport  fishing;  however,
                                          the  mere three percent  change  in bio-
                                         mass  indicates that  this negative
                                          impact might be minor relative to  the
                                         positive impacts  on  sport  fishing
                                         operating  through the other water
quality  components.   The most pronounced beneficial  impacts  are due to reduc-
tions  in impoundment sedimentation, impoundment  suspended  solids concentra-
tions, and  river  extinction coefficients.

     In  order  to  develop an aggregate estimate of  the impact of any practice
on any given benefit category,  the relationships between  the levels of the
various  water  quality components and the degree of benefit derived by each
user would  have to be defined.

     This could be done  possibly using an approach similar to that taken by
Meta Systems in assessing the impact of each alternative canal route of the
proposed Cross Florida Barge Canal on all the habitats of the canal zone —
as perceived by each interest group. 1 However, data constraints do not permit
these estimates , at least at this stage of the methodology development.
  RIVER LIGHT
  EXTINCTION COEFFICIENT OiJ
 IMPOUNDMENT LIGHT
 EXTINCTION COEFFICIENT  0
       <%)      -40
 IMPOUNDMENT BIOMASS
 1 Meta Systems Inc, The Overall  Assessment for the Cross Florida Barge Canal
Project. Contract No. DACW  17-75-C-0077,  U.S.  Army Corps of Engineers, Jack-
sonville District, Cambridge, Massachusetts,  May, 1976.
                                      59

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             TABLE 13.  RELATIVE IMPACTS OF CBWH PRACTICE ON HATER QUALITY COMPONENTS AND
                     BENEFIT CATEGORIES FOR THE LOWLAND SOIL TYPE •*

Percent Increase
Iron Base Case(CBWH)*
Benefit categories *"
human health
(drinking water)
municipal
water supply
dredging
(flood control)
ecology
recreation
sport fishing
contact
non-contact
aesthetics
local economy
Water Quality Components
Impoundment
Sedimentation
-70»
B E
0
+
+
+
•f
0
0
o
+
Impoundment
Sediment
Outflow
Concentration
-691
N E F
+
•fr
0
+
•f
+
+
+
+
River and
Impoundment
Nitrogen
-20%
I T
•f
+
0
+
0
+
0
0
•»•
River Light
Extinction
Coefficient
-59*
IMP
+
•f
0
•f
+
+
+
+
+
Impoundment
Light
Extinction
Coefficient
-18»
Impoundment
Biomass
-3\
ACTS
•f
+
0
+
•i
•f
+
+
•f
+
+
0
+
-
•f
+
+
+
    *   The base case is the highest revenue producing alternative (CB-CH). The effect on a benefit category of
    an increase in any parameter compared to the base case is noted as follows: detriment - -j  no effect  •  Oj
    benefit » +. A decrease would have the opposite sign (See Table 11).
    •*  See farm model discussion for definition of fanning practices.
    *** See text for explanation of benefit categories.

      If  an aggregate  measure can be  derived within  each benefit  category,  the
next  level of analysis  is the traditional benefit  analysis  striving for one
scalor to the extent  feasible.  This number would be the sum   of  the water
quality  impacts  (as weighted by each group) aggregated across  all the groups.
As discussed in the analysis of the  Cross Florida Barge Canal  and other pro-
jects, the major difficulty, perhaps the ultimate reason for the  inapplic-
ability  of the approach at the local/regional  level,  is the selection of the
various  weighting factors (based on  political,  social,  and economic aspects)
permitting the necessary aggregation.   Furthermore,  the fact that different
groups follow their interests implies  that computing an overall  scalor might
not be helpful in evaluating alternative agricultural practices  and their
various  impacts.

      If  an aggregate  measure of benefits to downstream users could be defined,
comparison with the aggregated costs incurred  by upstream farmers would lead
to a measure of net benefits.   However,  considering the fact that upstream
users incur different costs dependent  upon pertinent policies, locations,
soil, etc.,  the aggregate upstream cost does not reflect realities of conflict
among farmers.  These questions have not yet been adequately addressed within
the overall framework.
                                        60

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     Rather than attempting to account for all the considerations just men-
tioned, we have completed in Table 14 a simple summary of the relative impacts
of 11 farm practices  (each developed in a table similar to Table 13) on the
benefit categories of interest.  No attempt has been made here to weigh water
quality components or benefit categories.  We feel that while there are cer-
tain gains to be made in pursuing the traditional approach, it may be most
worthwhile in the short run  to examine possible non-monetary approaches that
allow for various weighting schemes to compare upstream and downstream bene-
fits and de-benefits associated with various uses (users).
   TABLE 14:  SUMMARY OF RELATIVE IMPACTS OF FARMING PRACTICES ON BENEFIT
                                 CATEGORIES
Soil Type: Lowlands Farming Practices*
Benefit
Categories**
human health
(drinking
water)
municipal
water sup-
ply
dredging
(flood con-
trol)
ecology
recreation
sport fish-
ing
contact
non-contact
aesthetics
local economy
CC-CV
K+) +
1(0)
4(-)
K+).
5(-)
5(0)
K-)
K+)
5(-)
1(0)
5(-)
K-)
1(0)
4(->
K+)
2(0)
3(-)
K+)
2(0)
3(-)
K+)
5(-)
CC-CH
2( + )
1(0)
3(-)
3(+)
3(-)
K+)
5(0)
3(+)
3(-)
4(+)
1(0)
K-)
2(+)
1(0)
3(-)
2(+)
2(0)
2(-)
2(+)
2(0)
2(-)
3(+)
3(-)
CC-NT
2( + )
1(0)
3(-)
3(+)
3(-)
K+)
5(0)
3(+)
3(-)
4(+)
1(0)
l(-)
2(+)
1(0)
3(-)
2(+)
2(0)
2(-)
2(+)
2(0)
2(-)
3(+)
3(-)
CB-CV
K + )
2(0)
3(-)
K+)
1(0)
4(-)
5(0)
K-)
K+)
1(0)
4(-)
1(0)
5(-)
K+)
2(0)
3(-)
K + )
2(0)
3(-)
K+)
2(0)
3(-)
K+)
1(0)
4(-)
CB-CH
6(0)
6(0)
6(0)
6(0)
6(0)
6(0)
6(0)
6(0)
6(0)
CB-NT
2(+)
KO)
3(-)
3(+)
3(-)
K+)
5(0)
3(+)
3(-)
4(+)
1(0)
l(-)
2(+)
1(0)
3(-)
2(+)
2(0)
2(~)
2(+)
2(0)
2(-)
3(+)
3(-)
CBWH
5( + )
KO)
6(+)
K+)
5(0)
6(+)
4( + )
1(0)
l(-)
5(+)
1(0)
4(+)
2(0)
4( + )
2(0)
6(+)
CBWH-NT
4( + )
2(0)
5( + )
1(0)
K+)
5(0)
5( + )
KO)
4(+)
2(0)
4(+)
2(0)
3(+)
3(0)
3(+)
3(0)
5(+)
1(0)
CC-CVT
K+)
2(0)
3(-)
K + )
1(0)
4(-)
5(0)
K-)
K+)
1(0)
4(-)
2(0)
4(-)
K+)
2(0)
3(-)
H+)
3(0)
2(-)
K+)
3(0)
2(-)
K+)
1(0)
4(-)
CC-CHT
2(+)
2(0)
3(-)'
3(+)
1(0)
K-)
K+)
5(0)
3(+)
1(0)
2(-)
4(+)
2(0)
2(+)
2(0)
2(-)
2(+)
3(0)
K-)
2(+)
3(0)
l(-)
3(+)
KO)
2(-)
CB-NTT
2(+)
1(0)
2(-)
3(+)
3(-)
K+)
5(0)
3(+)
3(-)
4(+)
1(0)
l(-)
2(+)
1(0)
3(-)
2( + )
2(0)
2(-)
2(+)
2(0)
2(-)
3(+)
3(-)
 *See farm model discussion for definition of farming practices.
 ** See text for explanation of benefit categories.
 +Sum of the effects on a genefit category of a change from the base case  (CB-
CH) to another farming practice.  Six water quality components are evaluated.
Numbers indicate the number of water quality component changes that have a
positive, negative, or no effect on the benefit category.  Detriment = -;
no effect = 0; benefit = +.
                                      61

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                                     72

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                                      73

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                                    74

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                                     75

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                                     76

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                             Appendix A




                             Farm Model









Introduction




     The development of the farm budget is presented in this appendix.




The model assumes that the farmer is a profit maximizer and will choose




the farming practice which gives him the highest net revenue.  The pur-




pose of the budget approach is to show the effects on net farm revenue




of different farming practices considered because of their potential




for reducing nonpoint source pollution for agriculture.  This model is




based on a farm budget developed by Dr. Klaus Alt of Iowa State Univer-




sity, Ames, Iowa and discussed in Appendix C "Economic Analysis Method-




ology" of USDA and U.S. EPA, Control of Water Pollution from Cropland,




Vol. II.






     The farm budgets shown here are based on eleven farming practices




which are appropriate for use on farms in the Black Creek area of north-




eastern Indiana near Fort Wayne.  The most commonly used cropping practices




in the case study are are included, corn and corn-soybean rotation, and




the most common method of cultivation, conventional tillage, which includes




fall plowing with a moldboard plow.  In addition to these practices, two




reduced tillage practices, chisel tillage and a no-till option, are applied




to these two cropping patterns to examine their effects on net revenue and




water quality.  Chisel tillage involves shredding stalks and chisel plowing





                                    77

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in the fall and disking in the spring.  The no-tillage option is defined




as shredding stalks in the fall and planting in the spring using a no-till




planter.  A more extensive crop rotation of corn-soybean-wheat-meadow,




involving field cover crops as well as row crops,  is also examined.




This rotation is considered with two  tillage options, one in which the




meadow is plowed in the fall using a  moldboard plow before planting the




corn in the spring and the other in which herbicides are used in the




spring to kill the remaining sod before planting corn with a no-till




planter.  Terracing, a structural erosion control measure, was added




to three of the above eight practices, continuous corn, both convention-




ally tilled and chisel tilled, and a  no-till corn-soybean rotation.






     Farm budgets were developed for  three typical farms of two hundred




and fifty acres each located on three soil types.  The soil types,




upland, ridge and lowland, were selected as representative of soils in




the case study region.  The uplands can be characterized as a Blount-




Morley-Pewamo association, the ridge  as a Rensselaer-Whitaker-Oshtemo




association and the lowlands as a Hoytville-Nappanee association.




Some of the farming practice costs vary depending on which soil type




the farm is located.






     Tables A-l through A-10 show detailed costs for the inputs,  ranging




from equipment to seeds, required for using each of the eleven practices




on each of the farms.   Table A-ll shows  expected yield and gross  revenue




for each practice and Table A-12  presents  a summary of all the costs as




well as gross and net revenue for each practice on each soil type.  The
                                   78

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practices are ranked in terms of net revenue in Table A-12  and in terms




of soil loss in Table A-13.





     Following this presentation of the basic farm budget model  for




the eleven practices considered  is the development of six alternative




situations and policies.  The use of the model here is to show how these




alternatives impact net farm revenue and in turn affect the choice of




the farmer.  Ultimately the implementation of any agricultural policy




will rest on the decisions made by the individual farmer.






     The assumption is made in Alternative A that the farmer  hires




custom operators to carry out certain tasks in the two extensive crop




rotation practices considered.  This results in increased net return for




these two practices.  The net revenues developed for these two




practices in this alternative are used in Section 6 of the main




report as part of the base case.  Custom hiring was not assumed  in




Alternatives B through F, following, which are preliminary.






     Alternative B represents a future scenario in which energy  prices




more than double compared to other prices.  This case was developed to




illustrate how the farm model can be used to examine the robustness of




agricultural policies under alternative futures.






     The last four alternatives, C, D, E and F illustrate the effects




of agricultural policies which might be implemented to encourage farmers




to adopt practices which are beneficial to water quality or which are




aimed directly at controlling farm factor inputs which are detrimental




to water quality.
                                    79

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                       Table A-l.   Terraces





     Terrace costs were calculated on the basis of cost per linear




foot of terrace as experienced in the Black Creek Project.  This




includes the cost of associated tile drains.  Since the slope




length is relatively short compared to the terrace spacing so that




there is one terrace per slope, as we assumed here, then the approx-




imate number of feet of terrace per acre is calculated by dividing




43,560 (the number of square feet per acre) by the terrace spacing.




This is the method suggested in the Midwest Farm Planning Manual




(Third edition, ISU Press, Ames, Iowa, 1973, revised 1975).






     While not the case in our study, if more than one terrace per




acre is specified, as in Table A-l, Appendix C, Control of Water




Pollution from Cropland, then the number of feet of terrace per




acre is estimated by dividing 43,560 by the slope length and mul-




tiplying by the number of terraces per slope.  Other items were




calculated as indicated in the footnotes.






     It was assumed for simplification purposes that every acre was




terraced.  It should be noted that the values used for terrace spacing,




slope length and cost per foot of terrace were generalizations applied




to the whole watershed area, and would vary considerably from farm to




farm in actual practice.
                                80

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                                 Table A-l
                              Terrace  Costs*
 Item
                                                        Amount
Terrace  spacing,  feet**                                  180
Slope  length, feet*                                      300
Number of terraces per slope*                              1
Feet of  terrace per acre                                 242
Construction cost per foot terrace  ($)***                  1.00
Construction cost per acre {$)                           242
Prorated construction cost ($)                            25.81
Maintenance cost, foot ($)                                 0.00011
Maintenance cost, acre ($)                                 0.03
Yearly terrace charge per acre  ($)                        25.84
Total  yearly terrace charge  (250 acres)  ($)            6,460.00
*    Assume slope length 300 feet and one terrace per slope.
**   Daniel McCain, District Conservationist, Allen County Soil
Conservation District, estimate for Black Creek Watershed.
***  James Lake, Black Creek Project Administrator, estimate for Black
Creek Watershed  ($1.00-$1.25).  Joseph Pedon,  Agronomist, Indiana
Soil Conservation Service, Indianappolis, recommended use of lower figure
to account for increased contractor experience over time.
+    Assume 15 year life (from Daniel McCain, District Conservationist,
Allen County Soil Conservation District) and interest at 8 percent.
Average yearly interest =  [(initial cost + salvage value)/2] x i rate.
Prorated construction cost = average yearly interest + [(initial cost)/
(economic life)].  Assume salvage value = 0.
++   Assumed one-half of maintenance cost used in Sidney James (ed.),
Midwest Farm Planning Manual, Third edition, ISU Press, Ames, Iowa,
1973, revised 1975, p. 34, after discussion with Joseph Pedon, Agronomist,
Indiana Soil Conservation Service.
                                    81

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              Table A-2.  Machinery Fixed Costs

     Specifications for the farm equipment for each farming
practice were developed using the equipment listed in Table 2,
Appendix C, Control of Water Pollution from Cropland as a base,
with modifications appropriate for current farming practices in
northeastern Indiana.  Discussions with local equipment dealers
and with Dr. Howard Doster, Dr. Harry Galloway and Dr. Donald
Griffith at Purdue University provided information for making
the modifications.

     There are many variations available to the farmer for each
item listed in Table A-2.  Here, an attempt was made to insure that
the equipment specified was appropriate for the soil conditions,
reflected current farming practices for a well managed farm,
including recent technology changes and was appropriately sized
so that, for example, the plow was not oversized compared to the
tractor.

     Current list prices for the farm machines were calculated,
for the most part, by averaging local equipment dealers estimates.
As a check, current Ames, Iowa prices were also obtained as well
as a national USDA price index which was used to update the prices
in Appendix C,  Control of Water Pollution from Cropland.

     Other items in Table A-2 were calculated as indicated in the
footnotes using data  from Appendix C, Control of Water Pollution
from Cropland, and from the Purdue Crop Budget.
                               82

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                                                            Table  A-2.   Machinery Fixed  Costs
CD
                                                                                             Yearly
Machine
Stalk Shredder
Moldboard Plow
Chisel Plow
Disk
Harrow
Sprayer
Planter
No-till Planter
wheat Drill
Cultivator
Combine
Platform
Corn Head
Hay Mower/
Conditioner
Hay Rake
Hay Baler
Initial List
Size & Other Specs. Price ($J*
12' flail
5-16"; high clearance;
sheer bolt
10'; three bar; straight
shank; pull type
20'; tandem; hydraulic
20'; hydraulic mounted
tractor mounted (rear);
120" boom size
4-30"; conventional;
no fertilizer attachments
4-30"; fluted coulters;
no fertilizer attachments
12'; with grass seeding
attachments
4-30"; rear mount
Small (70-80 hp) ; self-
propelled; diesel
13'; hydraulic; with
cutter bar
4-30"; picker-sheller
7'
Side delivery
PTO; 50-60 Ib bales;
rectangle bales; twine
3,050
4,000
2,150
6,750
750
1,400
4,000
5,500
4,250
2,000
27,100
3,850
7,800
4,800
1,250
5,100
Salvage**
Value (%)
13
17
13
17
17
17
17
17
9
17
18
18
18
12
12
21
.7
.7
.7
.7
.7
.7
.7
.7
.7
.7
.9
.9
.9
.5
.5
.1
Depreciation
Economic** (straight line
Life method)
12
10
12
10
12
10
10
10
14
10
10
10
10
12
12
8
219.
329.
154.
555.
51.
115.
329.
452.
274.
164.
2,197.
312.
632.
350.
91.
502.
35
20
62
53
44
22
20
65
13
60
81
24
58
00
15
99
Yearly
Taxes, Insurance*** Fixed
and Housing Interest Cost
137.
180.
96.
303.
33.
63.
180.
247.
191.
90.
1,219.
173.
351.
216.
56.
229.
25
00
75
75
75
00
00
50
25
00
50
25
00
00
25
50
138.71
188.32
97.78
317.79
35.31
65.91
188.32
258.94
186.49
94.16
1,288.88
183.11
370.97
216.00
56.25
247.04
495.31
697.52
349.15
1,177.07
120.50
244.13
697.52
959.09
651.87
348.76
4,706.19
668.60
1,354.55
782.00
203.65
979.53
          *    Prices are averages of local Indiana equipment  dealer  1977 estimates except for no-till planter price which is from the Department
              of Agricultural Economics, Iowa State University  for 1977.

          **   Table 2 Appendix C, Control of Water Pollution  from Cropland, Vol. II, U.S. Government Printing Office, Washington, D.C. ,  1976.

          ***  Taxes two percent, insurance one and a half percent of initial cost, Purdue Crop Budget,  p.  22;  housing one percent. Appendix C,  Table  2.

          +    Eight percent per year, Purdue Crop Budget, Department of Agricultural Economics,  Purdue  University, Lafayette, Indiana, 1977, p.  22.
              t(I+S)/2J r  =  yearly cost.

-------
               Table A-3.  Machinery Costs








     Data from Table  3,   Appendix C/ Control of Water Pollution




from Cropland, Vol. II, were used as the basis for this table.




The eight farm practices considered were developed from those




listed in Appendix C, Control of Water Pollution from Cropland,




Vol. II, with modifications so that they represented some of the




tillage practices and crop rotations used in the tillage trials




in the EPA Black Creek demonstration project.  Dr. Daniel McCain,




Allen County Soil Conservation District, and Mr. James Morrison




and Dr. Donald Griffith of Purdue University provided guidance for




the selection of the practices described in Table A-3.






    The practices were chosen to reflect the effects of changes




in tillage methods and changes in rotation of crops.  Continuous




corn and a corn-soybean rotation are each subjected to three




farming practices, conventional tillage, reduced tillage, and no




tillage.  A more extensive rotation consisting of corn, soybean,




wheat, meadow is also included, subject to two tillage practices,




one in which the meadow is plowed conventionally before the corn




is planted and the other in which the meadow is treated with herbi-




cide and the corn planted directly in the remaining sod.






    Tables A-4 through A-12 show eleven farming practices.  These




include the eight from Table A-3 plus three from Table A-3 with ter-




racing added:   continuous corn, conventional tillage; continuous




corn, chisel tillage; corn-bean rotation, no-till planting.
                               84

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    Hours per acre data were taken from Appendix C, Control of




Water Pollution from Cropland, Vol. II, and reviewed with Black




Creek project personnel.  Equipment specification changes made




some updated figures necessary; sources for updated figures are




noted on the table.  Most implements are used only once over the




field except for disking for chisel plow and hay harvesting equip-




ment.  For these implements the times over is variable and the




number shown is the average.  Total hours is equal to the product




of hours/acre, acres of use and times over.  Repair costs per




100 hours for the harrow were calculated from Appendix C  (Control




of Water Pollution from Cropland, Vol II) data to be three percent




and for the hay mower/conditioner, seven percent.
                              85

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                                                 Table  A-3.   Machinery Costs
CD
lapleaent
Hours/
Acre3
Acresfc Times Total
of use Overc hours
Repair
Cost/
100 hrs, S
Total
Yearly
Fixed Total
Corn, fall turn-plow, conventional
•oldboard plow6
harrow ~ "
sprayer
planter
cultivator
coabine ~~
Corn, fall shred stalks, chisel plow, spring disk
.36
.10
.10
.21
.21
.479
.479
250
250
250 ]
250 ]
250 ]
250 ]
250 ]
250 :
L 90
L 25
L 25
L 52.50
42.50
52.50
L 117. 5O
117.50
200
337
70
320
100
542
156
00
50
50
00
00
00
00
00
180
	 84_
36
136
52
636
183
00
38
63
75
00
50
85
30
697.52 877
1177.07 1261
120.50 126
244.13 280
697.52 815
346.76 401
4706.19 5343
1354.55 1537
10,643
45
13
JH) 	
04
85
stain
harroi
spray<
plant.
corn 1
Total
snreaaer 18
sr ~ ~" 21 	
»? TT79
lead 47g
250
250 ]
250
250 ]
250 ]
250
250 ]
L 45
L 52.
L.5 37
L.5 37
L 42
L 117
L 117
.50
50
50
.50
50
50
122
337
70
320
542
156
00
50
50
50
00
00
00
00
54
56
126
8
36
136
636
183
.90
.44
.56
.44
75
00
85
30
495
349
1177
120
244
697
4706
1354
31
15
07
50
_U
J.9
550
405
1303
128
280
815
5343
1537
10,365
21
59
63
94
88
04
85
66
stalk shredder
sprayer "
no-till planter
Notes (see following pages)
	 .18
	 .21
22
.479
250
250
250
250
1 45 122.00
1 	 52.50 	 70.00
1 	 55 	 440.00
1 	 117.50 	 542.00
1 117.50 156.00
242.00 959.09 1201.09
183.30 1354.55 1537.85

-------
                                                          Table A-3 (continued)
oo
Implement
Corn-soybeans, fall turn-plow, conventional
aoldboard plow
disk
harrow
sprayer
planter
cultivator
combine corn
coaLine soybeans
corn head
platform
Total
Corn-soybeans, fall shred, chisel plow, spring disk
stalk shredder
chisel plow 	
disk
harrow
sprayer 	
planter 	
combine corn
combine soybeans 	
corn head
platform 	
Total
Corn- soybeans, fall shred, no-till plant
stalk shredder 	 	 	
no-till planter 	 	 	
Hours/
Acre*

.36
.10
.10
.21
.17 'f
.21
.479
.30
.479
.30


.18
.2lh
.10
.10
.21
.17f
.479
.30
.479
.30


.18

Acres
of use15

250
250
250
250
250
250
125
125
125
125


125
250
250
250
250
250
125
125
125
125


125
250
Times
Overc

1
1
1
1
1
1
1
1
1
1


1

.5
.5








1
1
Total
hours

90
25
25
52.50
42.50
52.50
96 25

58.75
37.50


22.50
52.50
37.50
37.50
52.50
42.50
96.25

58.75
37.50


22.50
.55.00
Repair
Cost/
100 his,

200.00
337.50
22.50
70.00
320.00
100.00
542 00

156.00
77.00


122.00
107 . 50
337.50
22.50
70.00
320.00
542.00

156.00
77.00


122.00
440.00
Total
Repair
5d Cost, $

180.00
84.38
5.63
36.75
136.00
52.50
521 68

91.65
28.88


27.45
56.44
126.56
8.44
36.75
136.00
521.68

91.65
28.88


27.45
36 75
242.00
Yearly
Fixed
Cost, $

697.52
1177.07
120.50
244.13
697 . 52
348.76
4706 19

1354.55
668.60


495.31
349.15
1177.07
120.50
244.13
697 . 52
4706.19

1354.55
668.60


495.31
244 13
959.09
Total
Cost, S

877.52
1261.45
126.13
280.88
815.52
401.26
5227 87

1446.20
697 . 48
11,134.31

522.76
405.59
1303.63
128.94
280.88
815.52
5227.87

1446.20
697 . 48
10,828.87

522.76
280.88
1201.09
        Notes (see following pages)

-------
                                                   Table A-3 (continued)
oo
00
Implement
Corn-soybeans, fall shred, no-till plant (continued)
combine corn
combine soybeans
platform ~~ ~ ' " 	
Hours/
Acre3
.479
.30
.479
Corn-soybeans-wheat-meadow, fall turn-plow corn, fall shred, no-till
•talk shredder
•oldboard plow
•prayer -
no-till planter
wheat drill ~~ 	
combine corn
combine wheat
corn head
platform ~
hay mower/conditioner
hay rake
hay balei
Corn-soybeans-wheat-meadow, fall shred, no-till plant
•talk shredder
harrow ~ " ~ ' ~~
•prayer
no-till planter
wheat drill " ~
Motes (see following page)
.18
.36
.10
.10
.21
.22
.25
.479
.30
.479
.30
Ho
.63
.18
.10
.10
.21
.25

Acres Times Total
of useb Overc hours
125
125
125
plant
62
62,
125
125
125
125
62
62
62,
62
125
62
62
62
62
62
62
125
62.

1
- j 96
1 58
others
.5
5
5
5
.5 ]
_5 	
5
5
5
	 l±
	 12.
	 26_
27
	 15_
__
66
	
29
37
.5 74
.5 65
.5 137
11
	 6.
6.
	 1 26.
27.
5 15.

.25
ITS
.50
.50
.50
.50
63
88
38
50
38
63
81
50
63

Repair Total
Cost/ Repair
1OO hrs, Sd Cost, $
542.00
156. OO
122.OO
200.00
337.50
22.50
70.00
440.00
340.00
542.00
156.00
77.00
336.00
306.00
122.00
337.50
22.50
70.00
440.00
340.00

521
91
	 28
	 13
45
42
18
121
52
362
45
28
249
49
421
13
21
1
18
121
52

.68
.65
.73
.00
.19
.81
.38
.00
.22
.49
.83
.88
.22
73
09
41
38
00

Yearly
Fixed
Cost, $
4706
1354
495
697
1177
120
244
959
651
4706
1354
668
782
979
495
1177
120
244
959
651

.19
.55
.07
.13
.09
.87
.19
.60
	 i
31
07
13
09
87

Total
Cost, $
5227.87
1446.20
262.51
1080.09
704.09
1400.38
__697_.48_ 	
1198.16
121.91
262.51
1080.09 	
704.09


-------
                                                             Table A-3 (continued)
CO
Hours/
Implement Acre
Acres
of useb
Times Total
Overc hours
Repair Total
Cost/ Repair
100 hrs, 5d Cost, 5
Yearly
Fixed Total
Cost, 5 Cost, S
Corn- soybeans-wheat-meadow, fall shred, no-till plant (continued)
combine corn
combine soybeans
combine wheat
corn head
platform
hay mower/conditioner
hay rake
hay baler
Total
Notes:
a. Source: Table 3, Appendix C, Control of Hater Pollution
b. Acres on which implement is used each year.
c. Number of trips through field with implement.
d. Computed as percentage of list price. Used two percent
shredder: five oercent for moldboard olow. chisel olow.
479
30
30
479
30
349
30
63

from
62
62
62
62
125
62
62
62

.5
.b
.b
.b

.5
.5
.S

Cropland ,
for combine.
1
1
1
1
1
3.
3.
3.

unless
platform.

66.88

29.38
37.50
5 74.38
5 65.63
5 137.81

otherwise
corn head;

542

156
77
336
75

.00

.00
.00
.00
.00
306.00

noted.
three
^rcent

percent
for hay

362

4b
2ti
249
49
421

for
rake

.49

.83
.88
.92
.22
.70


4706.19

1354.55
668.60
782.00
203.65
979.53

harrow; four
, hay baler;

5068.68

1400.38
697.48
1031.92
252.87
1401.23
13,728.36
percent for stalk
seven percent for
                hay nower/conditioner;  eight percent for planters, wheat drill.  Source:  Table  3, Appendix C.
             e.  See Table  A-2 for equipment specifications.
             f.  Dr. Klaus Alt, ISU,  Ames, Iowa.
             g.  Midwest Farm  Planning Manual, p.  142.
             h.  Purdue Crop Budget,  p.  30.

-------
                     Table A-4.  Tractor Costs

     Tractor hours per acre were calculated by summing the hours

per acre given in Table A-5 for each machine pulled by" a  tractor  for

each practice considered.  The disk  and harrow were assumed to move

over the field in tandem  for all alternatives where they are used,

and to average 1.5 times  over the field annually for the C-B chisel

plow option.  Additional  times over  the field were also counted for

the haying operations such that each time an operation is carried

out (i.e. mowing, raking, baling) tractor usage is increased.  The

corn head and platform are attachments to the combine and so their

hours per acre were not included.  For the rotation options, hours

per acre figures were adjusted for some implements prior to summing,

to reflect the fact that  they are crop-specific and not used in all

years of the rotation (the "acres of use" column, Table A-3, accounts

for this adjustment factor).


     Of the 0.2 hours per acre added for fertilizer application,

0.1 is for N and 0.1 for  P and K application.  For the corn-bean

rotation, fertilizer is only applied once every two years so only

0.1 hours per acre were added.  For  the CBWM option, 0.125 hours

per acre were added because N, P, K  are applied once for corn and

beans and once for wheat  and K is applied once for the meadow.


     Other calculations were completed as indicated in the foot-

notes.  List prices are averages of  local dealer estimates.

Economic life was estimated using information from the Midwest

Farm Planning Manual based on the total annual tractor hours for

each option.
                               90

-------
                                                   Table A-4.   Tractor  Costs

I ten
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CB Conv. CB Chisel CB No-till Part
CBHM
. No-till
CBHM
No-till, Herb.
Terraces
C Conv. C Chisel
CB No-till
Tractor hours
oer acre" 1.72 1.59
Total tractor
hours 473.00 437.25
Tractor initial
costs, $C 23,600.00 23,600.00
Economic life,
yearsd 12 12
Salvage value,
percent® 25.5 25.5
Yearly depreci-
ation, $ 1,465.17 1,465.17
Taxes , insurance
t housing, $f 1,062.00 1,062.00
Average annual
interest, $' 1,184.72 1,184.72
Total fixed
costs, S 3,711.89 3,711.89
Repair costs,
sh 893.02 825.53
Total tractor costs,
$ (excl. fuel) 4,604.91 4,537.42
Notes: c « corn; CB - corn-bean; CBWM
1.28
352 . 00
23,600.00
13
23.5
1,388.77
1,062.00
1,165.84
3,616.61
664 . 58
4,281.19
1.54
347.27
23,600.00
13
23.5
1,388.77
1,062.00
1,165.84
3,616.61
655.65
4,272.26
1.32
363.00
23,600.00
13
23.5
1,388.77
1 , 062 . 00
1,165.84
3,616.61
685.34
4,301.95
= corn-bean-wheat-meadow.
a. Assume tractor is required for harvest hauling in amount equivalent
to tine requirements for combine-. Add 0.2 hours for application of
fertilizer with rented implements.
b. Increased by 10 percent for idling, travel to field, etc.
c. 100 PTO hp diesel (average of local Indiana equipment dealer
1.01
277.75
23,600.00
14
21.5
1,323.29
1,062.00
1,146.96
3,532.25
524.39
4,056.64
1.97
541.75
23,600.00
12
25.5
1,465.17
1 , 062 . OO
1,184.72
3,711.89
1,022.82
4,734.71
1
508
23,600
12
25
1,465
1,062
1,184
3,711
960
4,672
.85
.75
.00

.5
.17
.00
.72
.89
.52
.41
1.72
473.00
23,600.00 23,
12
25.5
1,465.17 1,
1,062.00 1,
1,184.72 1,
3,711.89 3,
893.02
4,604.91 4,
1
437,
600.
12
25,
465.
062
184.
711.
825.
537.
.59
.25
.00 23,

.5
.17 1,
.00 1,
.72 1,
.89 3,
.53
.42 4,
1.01
277.75
600.00
14
21.5
323.29
062.00
146.96
532.25
524.39
056.64
e. From Appendix C, Table 4, used values corresponding to appropriate
economic life.
f. Taxes 2%, insurance 1.5% of initial cost, Purdue Crop Budget, p. 22;
housing 1%, Appendix C, Table 2.
q. 8% per year, £ur.d.u.e Crop Budget, p.
22; yearly cost =[(I+S)/2]r.
    price estimates).
d.  From Sidney James  (ed.) Midwest Farm Planning  Manual, 3rd
    edition. Revised Printing, ISU Press, Ames,  Iowa, 1975,
    Table 4.7, p.  129.
h.   0.8%  of  list price per 100 hours of  use. Appendix C,  p.  182.

-------
                    Table A-5.   Fuel Costs




     Fuel costs were based on cost per hour for total tractor and




combine hours.  Tractor fuel costs were estimated according to a




standard formula, 0.044 times the maximum PTO hp.  Combine fuel




costs were more complicated to estimate since data on fuel consump-




tion are only available on a per acre basis and vary according to




the crop being harvested.  The formula used was gal./acre x I/




(hours per acre)  x $0.50/gal. x 1.15 (for lubrication costs).  For




the corn-soybean rotations and the corn-soybean-wheat-meadow rota-




tions the results using the above formula for each crop were




averaged.
                             92

-------
                                                      Table A-5.   Fuel  Costs

Item
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CBWM
CB Conv. CB Chisel CB No-till Part. No-till
CBWM
No-till, Herb.
Terraces
C Conv. C Chisel
CB No-till
Total tractor
hours
Fuel cost per trac-
tor hour, $a
Tractor fuel
coat, S
Total combine
hours
Fuel cost per ,
combine hour,S
Combine fuel
cost, $
Total fuel
cost, $
473.00
2.53
1,196.69
117.50
1.96
230.30
1,426.99
437.25
2.53
1,106.24
117.50
1.96
230.30
1,336.54
352.00
2.53
890.56
117.50
1.96
230.30
1,120.86
347.27
2.53
878.59
96.25
2.03
195.39
1,073.97
363 . 00
2.53
918.39
96.25
2.03
195.39
1,113.78
277.75
2.53
702.71
96.25
2.03
195.39
898.10
541. 75
2.53
1,370.63
66.88
2.06
137.77
1,508.40
508.75
2.53
1,287.14
66.88
2.06
137.77
1,424.91
473.00
2.53
1,196.69
117.50
1.96
230.30
1,426.99
437.25
2.53
1,106.24
117.50
1.96
230.30
1,336.54
277.75
2.53
702.71
96.25
2.03
195.39
898.10
Notes:  C * corn; CB - corn-bean; CBWM = corn-bean-wheat-meadow.
a.  Fuel consumption (diesel) gallons per hour = 0.044 x PTO hp.   Lubrication  costs at 15% of  fuel cost, George E. Ayres, Estimating Farm
    Machinery Costs, ISO Cooperative Extension Service, Ames,  Iowa,  November 1976, p. 8.  Assume diesel fuel at $0.50/gal., Purdue Crop
    Budget, p. 24.

b.  Fuel consumption (diesel) corn - 1.60 gal./acre;  beans,  wheat  =  1.10 gal./acre, George E.  Ayres, Fuel Required for Field Operations,
    ISU Cooperative Extension Service, Ames, Iowa, May 1976, p.  2.   Lubrication at 15% of fuel cost.  Assume diesel fuel at $0.50/gal.,
    Purdue Crop Budget, p. 24.

-------
                       Table  A-6.   Seed  Costs





     Seed costs are calculated from the estimated amounts of seed




applied per acre and the price of  seed per pound or bushel.  Seed-




ing rates for corn vary according  to soil type and tillage practice.




Wheat and hay seed amounts are constant for the two tillage prac-




tices involving them.  Soybean seed amounts are increased for reduced




tillage, but are insensitive to soil type.






     Seed cost per acre is calculated as the average for all years




of the rotation, if not continuous corn.  Total seed cost is deter-




mined for the whole farm based upon the  average annual seed cost




and the total acres farmed.
                               94

-------
                                                      Table A-6.  Seed  Costs
I tea
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CB Conv. CB Chisel CB No-till
CBHM
Part. No-till
CBHM
No-till, Herb.
Terraces
C Conv. C Chisel
CB Ni-tlll
cn
Seeding rate
(seeds/acre)
A uplands 20,OOO 20,000 21,000 20,000 20.OOO 21,000
B ridge 22,000 22,000 23,000 22,000 22,000 23,000
C lowlands 24,000 24,000 25,000 24,000 24,000 25,000
Seed amount, bu.°/acre
A uplands .238 .238 .250 .238 .238 .250
B ridge .262 .262 .274 .262 .262 .274
C lowlands .286 .286 .298 .286 .286 .298
Seed cost, $7acre
A uplands 9.52 9.52 10.00 9.52 9.52 10.00
B ridge 10.48 10.48 10.96 10.48 10.48 10.96
C lowlands 11.44 11.44 11.92 11.44 11.44 11.92
Wheat
Seed amount, bu.'vacre
Seed cost, SVacre
Hay
Seed amount, Ibs. /„,-,-„
Seed cost. $9 /.or.
Soybeans
Seed amount, bu.h/acre 1-00 1.00 1.05
Seed cost, SVacre 10.80 10.80 11.34
Seed cost per
acre , S*
"JTuplands 9.52 9.52 10.00 10.16 10.16 10.67
B ridge 10.48 10.48 10.96 10.64 10.64 11.15
r lowlands 11.44 11.44 11.92 11.12 11.12 11.63
20.0OO
22,000
24,000
.238
.262
.286
9
10
11
1
7
14
18
1
11
11
11
12


.52
.48
.44
.5
.13
.00
.12
.05
.34
.53
.77
.01
21,000
23,000
25,000
.250
.274
.298
10
10
11
1
7
14
18
1
11
11
11
12
20 , OOO 20 , 000
22,000 22,000
24,000 24,000
.238 .238
.262 .262
.286 .286
.00 9.52 9.52
.96 10.48 10.48
.92 11.44 11.44
.5
.13
.00
.12
.05
.34
.65 9.52 9.52
.89 10.48 10.48
.13 11.44 11.44
21,000
23,000
25,000
.250
.274
.298
10.00
10.96
11.92




1.05
11.34
10.67
11. IS
11.01
Notes (see following page)

-------
                                                                  Table A-6  (continued)
Item
Tillage Practices
C Conv. C Chisel
Total seed
cost, $
A upland 2,380.00 2,380.00
B ridge 2,620.00 2,620.00
C lowlands 2,860.00 2,860.00
Notes; C • corn; CB - corn-bean; CBHM
C No-till
Dotations
CB Conv.


2,500.00 2,540.00
2,740.00 2,660.00
2,980.00 2,780.00
CB Chisel


2,540.00
2,660.00
2,780.00
CB No-till Part


2,667.50
2,782.50
2,907.50
CBHM
. No-till


2,882.50
2,942.50
3 , 002 . 50
CBWM
No-till, Herb.


2,912
2,972
3,032
Terraces
C Conv.


50 2,380.00
50 2,620.00
50 2,860.00
C Chisel


2,380.00
2,620.00
2.860.00
CB No-till


2,667.50
2,782.50
2,907.50
" corn-bean-wheat-meado*.
            »•  Based on discussions with Dr. Donald Griffith, Purdue University; Rex Journey,  Allen County Soil Conservation  District.
            b.  Based on 84,000 seeds per bushel. Appendix C, Table 6.
VD          c.  Assume price of $40 per bushel, Adler's Seed, Kokomo, Indiana.
            
-------
                    Table A-7.  Fertilizer Costs



     Fertilizer costs are calculated from the estimated pounds per



acre application of N, P2°5 and K2° ^or corn' soybeans, wheat and



hay, and the price per pound of these fertilizers.  Fertilizer



application rates for corn vary according to soil type and tillage



practice.  Application rates are based upon discussions with the



individuals indicated in the footnotes and represent normal expected



application rates for the Black Creek area.  Lower yields are expec-



ted on the poorer upland soils and also less N fertilizer is normally



applied.  However, more P00  is applied there.  Ten percent more N is
                         & o


used for all no-till alternatives.  P90c applications for wheat and



soybeans on the uplands are increased in the same proportion as for



corn.  Wheat yields are not expected to vary according to location



(soil type) or tillage practice and, therefore, N application for



wheat is constant.  Since this is assumed to be a well-managed farm,



K_0 is applied to the hay as well as the other crops.  Soybeans in



the corn-bean rotation are expected to contribute 10 pounds of N per



acre to the corn.  Legumes in the corn-bean-wheat-meadow rotation



are expected to contribute 50 pounds of N per acre.  These fertilizer



application rates are appropriate for the Black Creek area.




     For the rotations, average annual fertilizer amounts are calcul-



ated and the prices applied to these figures.  Total fertilizer cost



is determined for the whole farm based upon these annual costs.



Total fertilizer  costs include the rental of  application equipment.



For calculating equipment rental costs it is  assumed that  the P and



K for the  soybeans are applied along with the corn  fertilizer  in  the




                                97

-------
corn year for corn-soybean rotation alternatives and also that N is




not applied to hay for corn-soybean-wheat-hay alternatives, so




equipment costs are correspondingly reduced.
                                98

-------
                                                              Table A-7.   Fertilizer  Costs
              I tea
                                        Tillage Practices
                                 C Conv.   C Chisel  C No-till
                                                                                         Rotations
                                                                                                    CBWM           CBWH
                                                                CB Conv.  CB Chisel  CB No-till  Part.  No-till   No-till, Herb.
                                                                                                                                       Terraces
                                                                                                                              C Conv.   C  Chisel  CB No-till
VD
              Corn
N , Ibs/acre
A uplands
B ridge
C lowlands
PjOs > Ibs/acre
A uplands
B ridge
C lowlands
125
160
160

44
40
40
125
160
160

44
40
40
137. 5d
176
176

44
40
40
115b
150
150

44
40
40
115b
150
150

44
40
40
126. 5d
165
165

44
40
40
75°
110
110

44
40
40
82. 5d
121
121

44
40
40
125
160
160

44
40
40
125
160
160

44
40
4O
126. 5d
165
165

44
40
40
              K2O, Ibs/acre
                                      50
                                                          50
              Hav.

              K2O, Ibs/acre
120
            120
              Wheat

              N, Ibs/acre
 60
             60
              P2O5, Ibs/acre
               A uplands
               B ridge
               C lowlands
 44
 40
 40
44
40
40
              K2O, Ibs/acre
                                                                                                          40

-------
                                                       Table A-7  (continued)
o
o

««* C Conv. C Ch
Soybeans
tiOf, Ibs/acre
A uplands
B ridge
C lowlands

Average Annual
aaount, Ibs/acre
N
A uplands 125
B ridge 160
C lowlands 160
A uplands 44
B ridge 40
C lowlands 40
MO 50
Cost of fertilizer
per acre, $•
A uplands 29.11
» ridg« 32.90
C lowlands 32.90

isel C No-till CB Conv. CB Chisel CB
11 11
10 10
10 10
70 70
125 137.5 57.5 57.5
160 176 75 75
160 176 75 75
44 44 27.5 27.5
40 40 25 25
40 40 25 25
50 50 60 60
29.11 30.74 18.11 18.11
32.90 34.98 19.90 19.90
32.90 34.98 19.90 19.90

Rotations 	
«"» CBWM
No-till Part. No-till No-till, Herb
11 11 11
10 10 10
10 10 10
70 70 70
63.25 33.75 35.63
82-5 42.5 45.25
82.5 42.5 45.25
27.5 24.75 24.75
25 22.5 22.5
25 22.5 22.5
60 70 70
18.85 15.39 15.63
20.88 16.11 16.46
20.88 16.11 16.46


11
10
10
70
125 125 63.25
160 160 82.5
160 160 82.5
44 44 27.5
40 40 25
40 40 25
50 50 60
29.11 29.11 18.85
32. 9O 32.90 20.88
32.90 32.90 20.88

-------
                                                       Table  A-7  (continued)

Item
Tillage Practices
C Conv. C Chisel C Mo-till
Rotations
CBHM
CB Conv. CB Chisel CB No-till Part. No-till
CBHM
No-till, Herb.
Terraces
C Conv. C Chisel
CB No-till
Total cost of
fertilizer, $
A uplands
B ridge
C lowlands
Rental of appli-
cation equipment,
ff
Total fertilizer
costs , $
A uplands
B ridge
C lowlands

7277.50
8225. OO
8225.00
262.50
7540.00
8487.50
8487.50

7277.50
6225.00
8225.00
262.50
7540.00
8487. SO
8487.50

7685.00
8745.00
8745.00
262.50
7947 . 50
9007.50
9O07.50

4527.50
4975.00
4975.00
131.25
4658.75
5106.25
5106.25

4527.50
4975.00
4975.00
131.25
4658.75
5106.25
5106.25

4712.50
5220.00
5220.00
131.25
4843.75
5351.25
5351.25

3847 . 50
4027.50
4027 . 50
153.13
4000.63
4180.63
4180.63

3907 . 50
4115.00
4115.00
153.13
4060.63
4268.13
4268.13

7277.50
8225.00
8225.00
262 . 50
7540.00
8487.50
8487 . 50

7277.50
8225.00
8225.00
262.50
7540.00
8487.50
8487.50

4712.50
5220.00
5220.00
131.25
4843.75
5351.25
5351.25
Notes:  C • com; CB • corn-bean; CBMM - corn-bean-wheat-meadow.
a.  Fertilizer amounts based on discussions with Dr. Harry Galloway,  Dr.  Donald Griffith,  Purdue University and Rex Journey, Allen County
    Soil Conservation District.
b.  Misuses 10 Ib credit from soybeans to corn, from discussions with Dr.  Harry Galloway,  Purdue University.
c.  Assumes 50 Ib credit from legumes to corn, from discussions with  Dr.  Harry Galloway, Purdue  University.

d.  Assume 10 percent increase in N application for all no-tillage  alternatives, from discussions with Dr.  Harry Galloway, Purdue University.
e.  Assume N as NH .  Prices per Ib are $0.13 for N, $0.19 for
                                                                 0 ,  and $O.O9 for  K20,  Purdue  Crop Budget.
f.  Assume $0.70/acre for NH  knife and $0.35/acre for 4-ton bulk spreader.  Appendix  C,  Table  7  figures  updated using USDA equipment price
    index from 1974 to 1976 of 1.415.

-------
                       Table  A-8.   Pesticide  Costs

     Pesticide costs were calculated based on recommended applications

of appropriate herbicides and insecticides for the soil types, tillage

practices and rotations considered.  The following factors were accounted

for:

     -No tillage options require more herbicides because no cultivation
      is used to destroy weeds.

     -The corn-bean-wheat-meadow alternative in which the corn is
      planted directly into the sod requires an additional type of
      herbicide to kill the remaining hay.

     -A different herbicide combination is used for corn than soybeans.

     -The corn-soybean rotation is assumed to prevent a corn rootworm
      problem but increases the likelihood of a cutworm problem.

     -Cutworm has a higher probability in no tillage options due to the
      amount of residue remaining.

     -Wireworm may be a problem where meadow is part of a rotation.

     -Insecticides are not generally applied to soybeans.


     For all the options considered, a risk averse  farmer is assumed,

who applies pesticides when there is a likelihood that they will be

needed.   In actuality, the use of the insecticides,particularly, will

vary from farm to farm depending on local conditions.


     Using current prices, cost per acre for each crop was calculated

and then multiplied by the number of acres which would be in that crop

in the rotation.  Total cost is the  sum  of the costs for each crop.
                                 102

-------
                                         Table A-8.   Pesticide Costs
o
CO
Tillage
I tea C Conv. C Ch
Corn, Amount*
Herbicide* (Lasso-
Atrex costo.), qt
A uplands 3. SO
B ridge 3.00
C lowlands 4.0O
Herbicide (Para-

-------
                                                  Table A-8.  (Continued)
Tillage
Ite" C Conv. c Ch


isel c No-till CB Conv. CB Chisel
Corn, Cost (continued)
Insecticide, $/acr« d 9.31 9.31 18.36 9 05
A uplands 5705.00 5705.00 11.285.00 2820.00
8 *i*9« 5225.00 5225.00 10.637.50 2580 00
C lowlands 6187.50 6187.50 11.930.00 3061.25
Soybeans, Cost
Herbicide. S/acreC
A uplands
B ridge
C lowlands
Total cost, $
A uplands
B ridge
C lowlands
Total Pesticide
Costs, $
13.76
10.46
16.90
125
1720.00
1307 . 50
2112.50

A uplands 5705.00 5705.00 11,285.00 4540. OO
B ridge 5225.00 5225.00 10,637.50 3887.50
C lowlands 6187.50 6187.50 11.930.00 5173.75
9.05
2820.00
2580.00
3061.25
13.76
10.46
16.90
1720.00
1307.50
2112.50
4540- 00
3887 . 50
5173.75
Not<>: c " corn' CB - corn-bean , CBNH - corn-bean-wheat-meadow.
a. Herbicide types and application rates based on discussions with Dr.
b. Insecticide types and application rates based on discussions with Dr
"•»-te illative it WAS Assunod that All insecticide p(
r** M^nxvaX COStS . T]T6AtBGnt I OX* ObSGlTVecl dABAQG And I

Rotations
	 — 	 . 	 Terraces
CBHM CBHM
CB No-till Part. No-till Nn-t-ill Hrrh f- r™, ,-,-. ,
	 *'" "«•"»• — <- ionv. c Chisel rb ::j-til!
	 9.05 	 17.14 	 17.14 	 9^31 g 31 9 Q5
4193.75 1915.63 2556.25 5705.00 5705.00 4193 75
3913.75 1795.63 2436.25 5225.00 5225.00 3913 75
4478.75 2036.25 2676 88 6187 SO fc]87 SO 4478*75
"-95 14.95 14.95 14 95
!!•« 11.49 11.49 u'49
18.24 	 18.24 	 18.24 	 18 ".24
125 62.5 62.5 125
1868.75 934.38 934.38 1868 75
1436.25 718.13 718.13 H36 25
2280.00 1140.00 1140.00 2280*00
6062.50 2850.01 3490.63 5705. OO 5705. OO 6062.50
5350.00 2513.76 3154.38 5225.00 5225.00 5350.00
6758.75 3176.25 3816 88 6187 50 6187 50 6758 75
Janes Williams, Purdue University.
. Thomas Turpin, Purdue University and Dr. David Pimental, Cornell Universi
                                                                                                            .
c.  L..so$4.05/lb active, Atrex  53.67/qt, Paraquat S8.75/qt, 2-4-D ester  Sl-50/pt, Indianapolis Fan. Bureau prices. Spring, 1977.
d.  Furadan $7/lb active. Counter S6.08/bl active. Indianapolis Farm Bureau; Lorsban »1.02/lb. Do- Chemical Co.,  Indianapolis,  Indiana.
e.  Lasso.$4.05/qt, Sencor $17.60.1b, Indianapolis Farm Bureau.

-------
                         Table A-9.  Labor Costs





     Labor costs are calculated from direct labor hours plus overhead and




hourly labor wage rates.  The direct labor hours are the sum of total




tractor hours plus total combine hours.   The overhead rate covers general




farm  overhead costs in addition to labor overhead.  An average farm wage




rate for Indiana was used.
                                   105

-------
                                                   Table A-9.   Labor  Costs

Item
Tillage Practices
C Conv. C Chisel C No-till

CBHM CBHM
CB Conv. CB Chisel CB No-till Part. No-till MO- Mil H...-K
Total direct
labor, hours* 590.50 554.75 469.50 464.77 459:25 374 00 600 63 s,b 63


590.50 554.75 374.00
Overhead  (30%),

hours
177.15 166.43
Total labor, hours 767.65 721.18
M
O
cn
Cost per hour, $
Total labor
costs, S
Notes; C - corn;
a. Tractor hours
b. Indiana Crop e
2.80 2.80
2149.42 2019.30
CB - corn-bean; CBWM =
plus combine hours.
md Livestock Statistics
140.85 139.43 137.78 112.20 182.59 172 69 177 IS IAA^I
610 -3S W4.20 597.03 486.20 7i>1.22 748.32 767.65 721 18
2.80 2.80 2.80 2.80 2.80 2 80 2 an ? ep
^708.98 1691.76 1671.68 1361.36 2215.42 2095.30 2149 42 2019 30
corn-bean-wheat-meadow .
_, Purdue University Agricultural Experiment station, A-iqust 1977. Table 89. "Farm u*o» B*t0* - ,





    .y         P   H
    average of Field and Livestock Workers and Machine Operators.

-------
                         Table A-10.  Other Costs




        Corn drying costs were estimated from the expected crop yield




and the costs of elevator drying.  It was assumed that all the corn




harvested would require an average of ten points of moisture removed.




It was assumed that soybeans did not require drying.









        Interest on operating capital was calculated for each item of




expense based on an annual interest rate of 8-1/2 percent.  Except for




fertilizer and labor costs the interest was charged for the period




indicated on the table for each item.









        Fertilizer costs were divided into nitrogen costs which were




assumed to be carried for about twelve months and phosphorous and




potash costs which were carried approximately eight months.  The




actual calculation was done as follows:  Fertilizer costs x 8/12 x




.085 x 1.35 (factor to account for differences in capital carrying




time)  - Fertilizer cost - .0765 = Interest on Operating Capital for




Fertilizer.









        Interest on operating capital for labor is based on a variable




labor force over the year; for example, additional labor required during




harvesting is not included in the interest calculation.  The calculation




was carried out as follows:   (Tractor hours - harvest hours) x 2.80 x 3/12




x .085 x 1.46 (adjustment factor) = Interest on Operating Capital for Labor.






        Total other costs are the sum of drying costs and interest costs.
                                   107

-------
                                                            Table  A-10.   Other  Coats
o
00
I ten
Corn Drying
Tillage Practices
C Conv.

Grain harvested, bu.
A uplands 26,250
B ridge 32,500
C lowlands 32.500
Cost per bu. , $• n if,
Total cost
A uplands
B ridge
C lowlands
4,200
5,200
5,200
Interest on -Operating
Capital"
Fertilizer (8 BO.)
A uplands 576.81
B ridge 649.29
C lowlands 649.29
Seed (8 so.)
A uplands
B ridge
C lowlands
Pesticide (6 BO.)
A uplands
B ridge
C lowlands
Fuel (3 so.)
Labor (3 BO.)
Total Interest
A uplands
B ridge
C lowlands
Total Other Costs
A uplands
B ridge
C lowlands
134.87
148.47
162.07
242.46
222.06
262.97
30.32
30 88
1,015.34
1,081.02
1,136.53
5,215.34
6,281.02
6,336.53
C Chisel
26,250
32,500
32 , 500
0 16
4,200
5,200
5,200
576.81
649.29
649.29
134.87
148.47
162.07
242.46
222.06
262.97
28.40
27 78
1,010.32
1,076.00
1,131.51
5,210.32
6,276.00
6,331.51
C No-till
24,937.50
32 , 500
26,OOO
0 16
3,990
5,200
4,160
607.98
689.07
639.07
141.67
155.27
168.87
479.61
452.09
507.03
23.82
1,273.45
1,340.62
1,409.16
5,263.45
6,540.62
5,569.16
notations
CB Conv.
13,781.25
17.062.50
17.O62.5O
2,205
2,730
2,730
356.39
390.63
390.63
143.93
150.73
157.53
192.95
165.22
219.88
22.82
737.90
751.21
812.67
2,942.90
3,481.21
3,542.67
CB Chisel
13,781.25
17,062.50
17,062.50
2,205
2,730
2,730
356'. 39
390.63
39O.63
143.93
150.73
157.53
192.95
165.22
219.68
23.67
740.11
753.42
814.88
2,9(15.11
3,483.42
3,544.88
CBUN
CB No-till Part. No-till
13,781.25
17,062.50
15,356.25
2,205
2,730
2,457
370.55
409.37
409.37
151.16
157.96
164.76
257.66
227.38
287.25
19.08
814.22
829.56
896.23
3,019.22
3,559.56
3,353.23
7,218.75
8,937.50
8,937.50
1.155
1,430
1,430
306.05
319.82
319.82
163.34
166.74
170.14
121.13
106.83
134.99
32.05
17.12
639.69
642.56
674.12
1,794.69
2,072.56
2.104.12
CBHH
No-till, Herb.
Terraces
C Conv.
7,218.75 28,000
8,937.50 34,250
8,490.63 34,250
	 0.16
1,155
1,430
1,358.50
310.64
326.51
326.51
16O.22
163.62
167.02
148.35
134.06
162.22
	 30.28
14.25
663.74
668.72
700.26
1,818.74
2,098.72
2.058.78
O.16
4,480
5,480
5,480
576.81
649.29
649.29
134.87
148.47
162.07
242.46
222.06
262 . 97
30.32
30.88
1,015.34
1,081.02
1,136.53
5,495.34
6,561.02
6,616.53
C Chisel
28,000
34,250
34,250
0.16
4,480
5,480
5,480
576.81
649.29
649.29
134.87
148.47
162.07
242.46
222.06
262 . 97
28. «
27.78
1,010.32
1,076.00
1,131.51
5,490.32
6,556.00
6,611.51
CB No-till
14,656.25
17,937.50
	 0.16
2,345
2,870
2 597
370.55
409.37
409.37
151.16
157.96
164 76
257.66
227.38
287.25
19.08
15.77
814.22
829.50
896.23
3,159.22
3,699.56
3,493.23
            Notes:   C • corn; CB - corn-bean; CBHH = corn-bean-wheat-»eadow.


            a.  Elevator drying costs for corn for 10 pts.  renoved, Purdue Crop Budget,  p. 6.


            b.  Assuse interest at 8.5 percent, Purdue Crop Budget, p. 8.

-------
                          Table A-11.  Revenue



     Gross revenue was calculated from the expected yield per acre for




each crop, the number of acres planted with each crop and the expected




price.  Expected yields for corn and soybeans vary according to soil




type and farming practice.  Crops on wetter soil types do not respond




as well to decreased tillage as on other soils.  Lower yields are




expected on the poorer upland soils for all tillage practices.  Rotations




tend to increase corn yields.  Hay yields are responsive to soil types




whereas wheat yields are not.  Tillage practices for wheat and hay do




not vary for the two rotations using them and so yields are not affected.




These yields are appropriate for the Black Creek area.  The addition of




terracing was assumed to create better drainage and to allow one week




earlier planting time with yield advantage of one bushel per day.








      It  should  be  noted  that gross  revenue  is,  of  course,  very sensitive




 to  the crop prices chosen.
                                      109

-------
Table A-ll.  Revenue
tillage

Ite» C Conv. C Ch
Corn
Expected yield.
bu/acre *
Practices

isel C No-till CB Conv.



A uplands 105 105 99.75 no 25
B ri(J9e 130 130 130 136 50
C lowlands 130 130 104 136.50
Total output, bu 	
A uplands 26,250 26,250 24,937.50 13,781.25
8 ridge 32,500 32,500 32,500 17,062 5O
C lowlands 32,500 32,500 26,000 17,067 25


CB Chisel



110.25
136.50
136.50
13,781.25
17,062.50
17,062.50
Rotations

CB No-till Pa



110.25
136.50
	 122.85
13,781.25
17,062.50
15,356.25

CBWM CBHH
rt. No-till No-till, Herb.



115.50 115.50
143 143
143 	 135.85
62.50 62.50
7,218.75 7,218.75
8,937.50 8,937.50
8,937.50 8 49O 63
c






112 112 117.
137 137 143.
250 250 125
28,000 28,000 14,656.
34,250 34,250 17,937.


25
50
25
50
?/bub 22222 2
A uplands 52,500 52,500 49,875 27,562.50
B ridge 65,000 65,000 65,000 34,125
C lowlands 65,000 65,000 52,000 34,125
Soybeans
Expected yield.
bu/acre*
A uplands
B ridge
C lowlands
Area cropped, acres
Total output, bu
A uplands
B ridge
C lowlands
Expected price.
S/bub
Gross Revenue , $
A uplands
B ridge
C lowlands



30
40
	 40
3,750
5,000
5,000

5
18,750
25,000
25,000
27,562.50
34,125
34,125



30
40
36
3,750
5,000
4,500

5
18,750
25,000
22,500
27,562.50
34,125
30,712.50



27
38
32
3,375
4,750
4,000

5
16,875
23,750
20,000
14,437.50 14,437.50
17,875 17,875
17,875 16 981 26



27 27
38 38
32 32
62.5 	 62.5
1,687.50 1,687.50
2,375 2,375
2,000 2,000

5 5
8,437.50 8,437.50
11,875 11,875
10,000 10,000
56,000 56,000 29,312.
68,500 68,500 35,875



29
40
3,625
5,000

-
18,125
25 OCO
21,250
50











-------
                                                      Table A-ll  (continued)

Item
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CB Conv. CB Chisel CB No-till
CBHH
Part. No-till
CBHH
No-till, Herb.
Terraces0
C Conv. C Chisel
CB No-till
Wheat
Expected yield.
bu/acre a
Area cropped, acres
Total output, bu
Expected price.
$/bu°
Gross Revenue, $
tav.
Expected yield.
tons/acre a
A uplands
B ridge
C lowlands
Area cropped, acres
Total output, tons
A uplands
B ridge
C lowlands
Expected price.
$/tond
Gross Revenue, $
A uplands
B ridge
C lowlands
TOTAL GROSS
REVENUE, $
A uplands 52,500 52,500 49,875 46,312.50 46,312.50 44,437.50
B ridge 65,000 65,000 65,OOO 59,125 59,125 57,875
C lowlands 65,000 65,000 52,000 59,125 59,125 50,712.50
Notes: C » corn; CB = corn-bean; CBWM = corn-bean-wheat-meadow.

45
62.50
2,812.50

2.50
7,031.25



3.50
4
4
62.50

218.75
250
250

60

13,215
15,000
15,000


43,031.25
51,781.25
49,906.25


45
62.50
2,812.50

2.50
7,031.25



3.50
4
4
62.50

218.75
250
250

60

13,125
15,000
15,OOO


43,031.25 56,000 56,000 47,437.50
51,781.25 68,500 68,500 60,875
49,012.50 68,500 68,500 53,712.50

a.  Yield levels based on discussions with Dr.  Harry  Galloway and Dr. Donald Griffith, Purdue University.  Yield reductions for  no-till
    are preliminary and »ay change with more information.   No-till yields are highly dependent on soil type and weed control.
b.  Purdue Crop Budget, Department of Agricultural Economics, Purdue University, Lafayette, Indiana, 1977, p.  7.
d.
    7 bu/acre corn yield advantage with terracing due to better drainage, 2 bu/acre soybean yiald advantage.
    Based on discussions with Rex Journey, Allen County Soil  Conservation District.

-------
                        Table A-12.  Summary




        This table is straightforward.  All costs were added for each




farming practice alternative and then subtracted from gross revenue to




give net return.  Land costs were not included since these were assumed




to be the same for each soil type no matter what farming practice is




used,  it should be noted, however, that when we eliminated land costs




from the summary calculation we eliminated a variable which might tend




to equalize return among farmers located on different soils.  For example,




an upland farm may have much lower land costs than a lowland farm which




might counterbalance the differences in net return.  Due to the use of




a percentage factor added to labor costs to cover farm overheads as well




as to the elimination of land costs, the net revenue values are most




useful for relative comparisons among alternatives rather than as measures




of actual profit.
                                  112

-------
                                                        Table A-12.   Suaury
I tea 	
Tillage Practice*
C Con*. C ChiMl C Mo-till
Botations
OHM
CB Conv. CB ChiMl CB no-till Part. Mo-till
cm*
No-till. Herb.
Terraces
C COB", C Chisel
CB No-till
Gross
B ridge
C lowlands
Costs
Tractor (excl .
fuel)
iBpleaants
(excl. fuel)
Fuel
Seed
B ridge
C lowlands
Fertilizer
A uplands
B ridqe
Pesticides
A uplands
B ridge
Labor 	
Terracing 	
Other
A uplands
B ridge
52,500
65,000
65,000
4.604.91
10,643.65
1.426.99
2,380
2,620
2,860
7,540
8,487.50
8.487.50
5,705
5,225
6,187.50
2.149.42
5,215.34
6,281.02
6,336.53
Total Cost (Net of
Land Cost)
A uplands 39,665.31
B ridge 41,438.49
C lowlands 42,696.50
Net Return (Excl
Land Costs)
A uplands
B ridge
C lowlands
-
12,834.69
23,561.51
22,303.50
52,500
65,000
65,000
4.537.42
10,365.66
1,336.54
2,380
2,620
2,860
7,540
8,487.50
8,487.50
5,705
5,225
6,187.50
2.019.30
5,210.32
6,276
6.3:1.51
39,094.24
40,867.42
42,125.43
13,405.76
24,132.58
22,374.57
49,875
65,000
52.0OO
4,281.19
8,913.07
1,120.86
2,500
2,740
2,980
7,947.50
9,007.50
9.007.50
11,285
10,637.50
11,930
1,708.98
5,263.45
6,540.62
5,569.16
43,020.05
44,949.22
45,510.76
6,854.95
20,050.78
6,489.24
46,312.50
59,125
59,125
4,272.26
11,134.31
1,073.97
2,540
2,660
2,780
4,658.75
5,106.25
5,106.25
4,540
3,887.50
5,173.75
1,691.76
2,942.90
3,481.21
3,542.67
32,853.95
33,307.01
34,774.97
13,458.55
25,817.99
24,350.03
46,312.50
59,125
59,125
4,301.95
10,828.87
1,113.78
2,540
2,660
2,780
4,658.75
5,106.25
5,106.25
4,540
3,887.50
5,173.75
1,671.68
2,945.11
3,483.42
3,544.88
32,600.14
33,053.45
34.521.16
13,712.36
26,071.55
24,603.84
44,437.50
57,875
50,712.50
4,056.64
9,376.28
898.10
2,667.50
2,787.50
2.907.50
4,843.75
5,351.25
5,351.25
6,062.50
5,350
6,758.75
1,361.36
3,019.22
3,559.56
3.352.23
32,285.35
32,740.69
34.063.31
12.152.15
25,134.31
16,649.39
43,031.25
51,781.25
49,906.25
4.734.71
14,493.38
i. son. 40
2,882.50
2,942.50
3,002.50
4,000.63
4,180.63
4,180.63
2,850.01
2,513.76
3,176.25
2.215.42
1,794.69
2,072.56
2.104.12
31,479.74
34,661.36
35,415.43
8,551.51
17,119.89
14,490.82
43,031.25
51,781.25
49,012.50
4,672.41
13,728.36
1.424.91
2,912.50
2,972.50
3,032.50
4,060.63
4,268.13
4,268.13
3,490.63
3,154.38
3,816.88
2-095.30
1,818.74
2,098.72
34,203.48
34,414.71
35,097.27
8,827.77
17,366.54
13,915.23
56,000
68,500
68,500
4,604.91
10,643.65
1,426.99
2,380
2,620
2,860
7,540
8,407.50
8,487.50
5,705
5,225
6,187.50
2,149.42
5,495.34
6,561.02
6.616.53
46,405.31
48,178.49
49,436.50
9,594.69
20,321.51
19,063.50
56,000
68,500
68,500
4,537.42
10,365.66
1,336.54
2,380
2,620
2,860
7,540
8,487.50
8.487.50
5,705
5,225
6,187.50
2,019.30
5,490.32
6,556
6.611.51
45,834.24
47,607.42
48,365.43
10,165.76
20,892.58
19,634.57
47,437.50
60.875
53.712.50
4.056.64
9.376.28
898.10
2.667.50
2,787.50
2,907.50
4,843.75
5,351.25
5.351.25
6,062.50
5,350
6,758-75
1,361.36
6,460
3,159.22
3,699.56
3.493.23
38,885.35
39,340.69
40,663.11
8,552.15
21,534.31
13,049.39
        C - corn;  CB  » corn-bean; CBHM - corn-bean-wheat-meadow.

-------
                    Table A-13.  Net Revenue Ranking



     Table A-13  shows the ranking of the  farming practice options




according to net revenue, from the highest revenue producing alternative




to the lowest.  The rankings are shown for each soil type and also for




all- soil types simultaneously.






     For all soil types the corn-soybean chisel plow option is the best,




better than conventional tillage although only slightly better.  Since




gross revenues are the same for both of these options, the difference




is caused by the slightly lower equipment costs for the chisel plow




option (see Table A-12, Summary).






     It is interesting to note that the corn-soybean rotation options




using chisel and conventional tillage produce more revenue than continuous




corn.   This is not primarily due to a favorable corn-soybean price ratio as can




be seen from the gross revenue rows in Table A-12.  The difference is  caused,




in large part, by the higher fertilizer and pesticide costs which the




addition of soybeans in the rotation helps to reduce.   Labor hours are




also a factor because harvesting soybeans is quicker than harvesting corn.







     The no-till options, for both the corn-soybean rotation and continuous




corn,  produce less revenue (much less for continuous corn on the uplands




and lowlands)  than conventional or chisel tillage.  This is caused by




two factors,  a lower yield combined with high pesticide costs.  The




extra pesticide is needed to kill weeds which are more abundant due to




lack of plowing and to eradicate insects which the residue tends to



encourage.  The no-tillage options are more suited to better drained soils




as illustrated by the very good yield for the corn-soybean no-tillage  option






                                  114

-------
for the ridge soils.in Table A-ll and the correspondingly high net revenue ranking.







     The corn-soybean-wheat-meadow rotation options produce less revenue




than the corn-soybean and continuous corn options, generally.  Even though




many costs such as for pesticides are lower for these options and though




corn yields are quite high (see Table A-ll), the loss of revenue from put-




ting half the acreage into wheat and hay instead of corn or corn and




soybeans is so great that the net  return for these options is low.




Equipment costs are also very high for these rotation options (see




Alternative A).






     The terrace options produce lower net revenue than the other options




because the cost of installing terracing is not outweighed by the yield




advantage gained by improved drainage.  The terrace options follow the




same pattern as the non-terraced options, chisel plowing being more




lucrative than conventional tillage and that in turn better than no-




tillage except for the ridge farm where the yield advantage of the better




drained soils makes this option more attractive.






     When all soils are considered together it can be seen that the




ridge soils, generally speaking, produce the most revenue, although




there is not much of a difference between ridge and lowland soils for




conventional and chisel tillage.  The small differences between these




soils for these two tillage practices is caused by the slightly higher




seed and pesticide cost borne by the lowland farms.  When the no-tillage




practice is employed there is a greater difference in yields between




the ridge and lowland soils,  caused mainly by the lowered yields on the




lowlands.  The upland soils are much poorer than the other two soils and





                                    115

-------
 are associated with a much lower yield resulting in consistently lower




 net revenues for all fanning practices except no-tillage on the lowlands.




 This practice is just not suited to a wet,  poorly-drained soil,  so its




 poor performance is reflected in a very low net return.
high
low
                 Table A-13.  Net Revenue Ranking
                        Ridge
Lowlands
All Soils

h CB Chisel CB Chisel CB Chisel r
CB Conv. CB Conv. CB Conv. r
C Chisel CB No-till C Chisel r
C Conv. C Chisel C Conv. 1
CB No- till C Conv. C Chisel-Ter 1
C. Chisel-Ter. CB No-t.-Ter. C Conv.-Ter. r
C Conv.-Ter. c Chisel-Ter. CB No-till r
CBWMr-Herb. c Conv.-Ter. CBWM-Part. 1
CB No-t.-Ter. c No- till CBWM-Herb. 1
CBWM-Part. CBWM-Herb CB No-t.-Ter. r
C No-till CBWM-Part. C No-till r
r
r
1
1
r
r
1
1

1
u
u
u
1
u
u
u

u
u
u
u
u
1

«- 27
CB Chisel
CB Conv.
CB No-till
CB Chisel «- 25
CB Conv.
C Chisel
C Conv. «. 23
C Chisel
C Conv.
CB No-till-Ter.^21
C Chisel-Ter.
C Conv.-Ter.
C NO-till ^ 2Q
C Chisel - Ter.
C Conv.-Ter.
CBWM-Herb.
CBWM-Part. «- 17
CB No-till
CBWM-Part. ^ , .
•<- 14
CBWM-Herb.
CB Chisel
CB Conv.
C Chisel
CB No-till-Ter. 13
C Conv. "*"
CB No-till
C Chisel-Ter.
•*• 10
C Conv.-Ter.
CBWM-Herb
CB No-till-Ter.
CBWM-Part. ^_ fi
C No-till
C No-till , ,
•*- 6
,000
f V *^\S


,000


,000


,000

,000



,000


,000




,000



,000



,000

,000
   Notes:  C = corn; CB = corn-bean; CBWM = corn-bean-wheat-meadow.




           r = ridge; 1 = lowlands; u = uplands.





                                    116

-------
                        Table A-14.   Soil  Loss  Ranking




     Table .A-14  shows  the  farming practice  options  ranked according to  level




of soil loss, expressed in tons per acre, from low losses to high losses.




As one would expect, the corn-soybean-wheat-meadow options with half the




acreage in a grass cover crop have the lowest soil losses for each of the




soil types considered.  The partially plowed CBWM option loses more soil




than the herbicide option since plowing turns under the meadow sod.  The




no-tillage practice lowers runoff because more residue remains to retain




the water.  Terracing is a structural measure which prevents water from




flowing off the field as quickly as it otherwise would.  Chisel plow options




also produce less soil loss than conventional tillage options since more




residue remains after chisel plowing than after moldboard plowing which




turns the soil completely over.  Soil loss on the corn-soybean rotations




is higher than on the continuous corn options because soybean residue is




not as bulky as corn residue.






     Taking all the soils together and ranking the farming practices,




shows that, as one would predict, soil loss is greatest for the more




erosive upland soils with the greatest slope, less for the ridge, and




lowest for the lowlands which have almost no slope.   The range of soil




loss is quite large, going from less than one ton per acre lost from the




corn-soybean-wheat-meadow option on the lowlands to almost 28 tons per




acre from the conventionally tilled corn-soybean rotation on the uplands.





      As  indicated in the footnote on Table  A-14, the column  showing tons




per  acre of  soil lost  from the farming practice options can  be used to




visualize the  effects  of a soil loss restriction policy.   If a limit
                                    117

-------
were set at two tons per acre, for example, then all the practices ranked




below that limit would not be allowed.  This policy would have an unequal




effect on farms depending on where they are located.  It would force all




farms located on the uplands and ridge to move to a meadow rotation (this




conclusion  assumes,  of course, that all rotation possibilities available




to the farmer have been considered in our ranking).  Referring back to




Table A-13,  Net Revenue Ranking, it can be seen that the farm located on




the lowlands would make out the best in terms of profit under such a




policy.  In fact, farmers owning lowlands would probably experience wind-




fall gains in the short term since their land would become relatively much




more valuable.  Such a farmer could still use his most profitable option,




a chisel plowed corn-soybean rotation.  Farmers on the ridge would be




forced to switch to one of ther lowest net revenue options; they would




lose the most revenue under such a policy.  Farmers on the uplands would




also lose revenue by switching to a less profitable option.  Although they




would have the lowest net revenue under this soil loss restriction policy,




they also made less in the unrestricted case.
                                    118

-------
         Upland
                                Table A-14
                             Soil Loss  Ranking
Lowland
All Soils
Low CBWM-Herb. CBWM-Herb. CBWN-Herb. 1
CBWM-Part. CBWM-Part. CBWM-Part. r
C No-till C No-till C No-till 1
CB No-t.-Ter. CB No-t.-Ter. CB No-t.-Ter. 1
C Chisel-Ter. C Chisel-Ter. C Chisel-Ter. 1
CB No-till CB No-till CB No-till 1
C Chisel C Chisel C Chisel u
CB Chisel CB Chisel CB Chisel 1
C Conv.-Ter. C Conv.-Ter. C.Conv.-Ter. 1
C Conv. C Conv. C Conv. r
high CB Conv. CB Conv. CB Conv. 1
1
r
r
r

1
1
r

r
u

r
r
u

u
u

r
r
u
u
u

u
u
u

CBWM-Herb .
CBWM-Herb.
CBWM-Part
C No-till
CB No-till-Ter^
C Chisel-Ter.
CBWM-Herb .
CB No-till
C Chisel
CBWM-Part.
CB Chisel
C Conv.-Ter.
C No. till
CB No-till-Ter.
C Chisel-Ter.
•«-
C Conv.
CB Conv.
CB No-till
•«-
C Chisel
CBWM Part. ^_
•<-
CB Chisel
C Conv.-Ter.
C No-till ^
•«-
CB No-till-Ter.
C Chisel-Ter. ,
•<-
C Conv.
CB Conv.
CB No-till
C Chisel
CB Chisel
•<-
C Conv.-Ter.
C Conv.
CB Conv. ,
•4-




1 ton*





2 ton




3 ton



4 ton


5 ton



8 ton


9 ton

10 ton



16 ton



28 ton
Notes:  C  =  corn; CB = corn-bean; CBWM = corn-bean-wheat-meadow.

        r  =  ridge; 1  =  lowlands; u  =  uplands.

*    If soil loss restrictions of the tonnages given per acre were imposed,
then only the farming practices on the soils indicated located above the arrow
would be permissible.
                                      119

-------
                 Alternative A:  Custon Wheat, Hay




     This alternative was designed to examine the effects of using




custom operations instead of purchasing wheat and hay equipment.  It




was chosen because it appeared that the base case assumption, that a




farmer moving to a corn-soybean-wheat-hay rotation would purchase




specialized equipment for planting wheat and harvesting hay, was some-




what unrealistic.  This is especially true since the hay is only grown




on one quarter of the farm acreage.  In fact, the farmer most probably




would hire in help and equipment to carry out these operations for him.




This was the assumption made in the "Alternative A tables.






     Table A.-3A  lists  the  equipment used  in the two  corn-sdybean-wheat-




hay options along with the custom operations and their costs which




would be substituted for some of the equipment in the base case example.




The rates listed are averages for Northern Indiana and come from the




Cooperative Extension Service.  The table shows the total equipment and




custom operation cost for each alternative which may be compared with




the totals in Table A-3.






     The total tractor hours for the custom alternatives would not be




the same as for the two base case wheat, hay options because of the




equipment changes discussed above.  Fewer tractor hours would be required




to haul fewer implements.   Table A-4A shows the altered tractor hour per




acre figure and traces the resulting tractor cost charges.  Fuel cost




would be similarly affected and this is shown in Table A-5A.  Labor costs




are dependent on tractor hours and are therefore also lowered with the




addition of the custom operations.  This is illustrated in Table A-9A.







                                   120

-------
                Talble A-3A.   Machinery Costs — Custom Wheat,  Hay Alternative


Item 	 	 	
Corn- soybeans-wheat-meadow, fall turn-plow corn, fall
shred, no-till plant others
stalk shredder
mold board plow
disk
harrow

sprayer
no-till planter
combine corn, soybeans, wheat
corn head
platform
custom drilling wheat and meadow
custom hay mowing/conditioning, one operation
custom raking hay
custom baling hayc — uplands
— ridge and lowlands
Total — uplands
— ridge and lowlands
Corn- soybeans-wheat-meadow, fall shred, no-till plant
stalk shredder
disk
harrow

sprayer
no-till planter
combine corn, soybeans, wheat
corn head
platform
custom drilling wheat and meadow
custom hay mowing/conditioning, one operation
custom raking hay
custom baling hay — uplands
— ridge and lowlands
Total — uplands
— ridge and lowlands 	
a. From Table 3.
h Source: Indiana Custom Rates, EC-130 (Rev.), Cooperative
Total
non-custom
cost, $a


509.04
742.52
1,219.26
123.31
262.51

1,080.09
5,068.68
1,400.38
697.48
-
-
-
:
11,103.27
11,103.27
509.04
1,198.16
121.91
262 . 51
1,080.09
5,068.68
1,400.38
697.48
-
_
-
10,338.25
10,338.25

Custom
cost per
acre, $b


—
-
-
-
_

-
-
-
-
3.63
5.63
2.66
25.34
28.97
37.26
40.89
.
-
-
—
-
-
-
3.63
5.63
2.66
25.34
28.97
37.26
40.89

Extension Service, Purdue

Custom
acres


—
-
-
-
_

-
-
-
-
62.5
62.5
62.5
62.5
62.5
62.5
62.5
_
-
-
_
-
-
-
62.5
62.5
62.5
62.5
62.5
62.5
62.5

University,
Total
Custom
cost, $



-
-
-
_

—
—
—
-
226.88
351.88
166.25
1,583.75
1,810.63
2,328.75
2,555.63

-
-
_
-
—
—
226.88
351.88
166.25
1,583.75
1,810.63
2,328.75
2,555.63


Total
cost, $


509.04
742.52
1,219.26
123.31
262.51

1,080.09
5,068.68
1,400.38
697.48
226.88
351.88
166.25
1,583.75
1,810.63
13,432.02
13,658.90
509.04
1,198.16
121.91
262.51
1,080.09
5,068.68
1,400.38
697.48
226.88
351.88
166.25
1,583.75
1,810.63
12,667.00
12,893.88

West Lafayette, Indiana, 19
Rates given are average  1976 prices for Northern Indiana; Black Creek Watershed is in Northern Indiana.




Custom hay baling rate  from above source is $0.21 per 58 Ib bale so rates given vary according to yield variations.

-------
                             Table  A-4A

             Tractor Costs — Custom Wheat,  Hay Alternative
Item
a
Tractor hours per acre
Total tractor hours
Tractor initial costs, $
Economic life, years
Salvage value, percent
Yearly depreciation, $
Taxes, insurance & housing, $
b
Average annual interest, $
Total fixed costs, $
Repair costs, $
Total tractor costs, $
(excluding fuel)
CBWM
Partial
No-till
.79
217.25
23,600.00
14
21.5
1,323.29
1,062.00
1,146.96
3,532.25
410.17
3,942.42
CBWM
No-till
Herbicide
.68
187.00
23,600.00
15
19.5
1,266.53
1,062.00
1,128.08
3,456.08
353.06
3,809.14
Notes;  CBWM = corn-bean-wheat-meadow.

a.   Tractor hours per acre from Table A-4, minus hours per acre for
     implements replaced by custom operations.

b.   See footnotes to Table A-4.
                                 122

-------
                          Table A-5A



              Fuel  Costs  —  Custom Wheat,  Hay Alternative
Item
a
Total tractor hours
Fuel cost per tractor hour, $
Tractor fuel cost, $
Q
Combine fuel cost, $
Total fuel cost, $
CBWM CBWM
Partial No-till
No-till Herbicide
217.25 187.00
2.53 2.53
549.64 473.11
137.77 137.77
687.41 610.88
Notes:  CBWM = corn-bean-wheat-meadow.




a.   From Table A-^5.




b.   See footnotes to Table A-5.




c.   Derivation shown in Table A-5.
                                 Table  A-9A




                  Labor Costs — Custom Wheat, Hay Alternative
Item
Total direct labor, hours*
Overhead (30 percent), hours
Total labor, hours
Cost per hour, $*
Total labor costs, $
CBWM
Partial
No- till
284.13
85-^4
369.37
2.80
1,Q34.24
CBWM
No-till
Herbicide
253.88
76.16
330.04
2.80
924.11
     Notes:  CBWM = corn-bean-wheat-meadow.




     *   See footnotes to Table A-9.  Tractor hours from Table A-4A.
                                   123

-------
Since  fuel and labor costs have been decreased, interest on operating




capital for financing these input factors is correspondingly decreased,




as shown in Table  A-lpA.






     Table A-12A  summarizes all the changes discussed above and  shows a




new net revenue figure for each corn-bean-wheat-hay rotation.  Hiring in




custom operators yields approximately  a 45 percent increase in  revenue




for a farm located on the upland soils and about an 24 percent increase




for a farm on the ridge or lowland soils.






     The increase in net revenue produced by substituting custom oper-




ations for purchase of certain equipment results in an improvement in




position of the two wheat, hay rotations in comparison to the other




farming practices considered.   If Table A-13A is compared with Table A-13,




Net Revenue Ranking, it can be seen that the CBWM options move up on




the ranking list for each soil type, from 7 and 9 to 5 and 6 for the




uplands farm, from 10 and 11 to 7 and 9 for the ridge and from 8 and 9




to 7 and 8 for the lowlands.   In the ranking for all soils, the highest




CBWM option moves from the sixteenth to the eleventh spot.  It can be




concluded  from this comparison  that although the substitution  of




custom operations for the purchase of wheat and hay equipment certainly




improves the attractiveness of this rotation option in comparison to




the more common farming practices, it alone does not improve net revenue




enough to put it in a competitive position.
                                   124

-------
             Other Costs -
 Table A-10A




- Custom Wheat, Hay Alternative
Item
Corn drying Costs
A uplands
B ridge
C lowlands
b
Interest on Operating Capital
Fuel (3 months)0
Labor (3 months)
Other interest6
A uplands
B ridge
C lowlands
Total interest
A uplands
B ridge
C lowlands
Total Other Costs
A uplands
B ridge
C lowlands
CBWM
Partial
No-till
1155
1430
1430
14.61
13.06

590.52
593.39
624.95

618.19
621.06
652.62
1,773.19
2,051.06
2,082.62
CBWM
No-till
Herbicide
1155
1430
1358.50
12.98
10.43

619.21
624.19
655.75

642.62
647.60
679.16
1,797.62
2,077.60
2,037.66
Notes: CBWM = corn-bean-wheat-meadow.
a.   Derivation shown in Table A-10.




b.   See footnotes to Table A-10.




c.   Fuel costs from Table A-5A.




d.   Labor costs, from Table   A-9A.




e.   From Table A-10.





                                125

-------
                             Table A-12A

                Summary — Custom Wheat, Hay Alternative
Item
CBWM
Partial
No-till
CBWM
No-till
Herbicide
 Gross revenue, $*

 A uplands
 B ridge
 C lowlands

 Costs

 Tractor (excluding fuel)**
 Implements (excluding fuel)***
  A uplands
  B ridge
  C lowlands
 Fuel"1"
 Labor"1"1"
 Drying  and interest costs+++
  A uplands
  B ridge
  C lowlands
 Other Costs*
  A uplands
  B ridge
  C lowlands
 Total cost (net of  land cost)
  A uplands
  B ridge
  C lowlands

Net return  (excluding land costs)

A uplands
B ridge
C lowlands
                                       43,031.25
                                       51,781.25
                                       49,906.25
                                        3,942.42

                                       13,432.02
                                       13,658.90
                                       13,658.90
                                          687.41
                                        1,034.24

                                        1,773.19
                                        2,051.06
                                        2,082.62

                                        9,733.14
                                        9,636.89
                                       10,359.40

                                       30,602.42
                                       31,010.92
                                       31,764.99
                                      12,428.83
                                      20,770.33
                                      18,141.26
                 43,031.25
                 51,781.25
                 49,012.50
                  3,809.14

                 12,667.00
                 12,893.88
                 12,893.88
                    610.88
                    924.11

                  1,797.62
                  2,077.60
                  2,037.66

                 10,463.76
                 10,395.01
                 11,117.51

                 30,272.51
                 30,710.62
                 31,393.18
                12,758.74
                21,070.63
                17,619.32
Notes:   CBWM = corn-bean-wheat-meadow.

*   From Table A-12.
**  From Table A-4A.
*** From Table A-3A.
+    From Table A-5A.

++   From Table A-9A.

+++  From Table A-10A.
                              126

-------
                                  Table  A-13A




              Net Revenue Ranking — Custom Wheat, Hay Alternative
         Uplands
Lowlands
All Soils
high CB Chisel CB Chisel CH Chisel r
CB Conv. CB Conv. CB Conv. r
C Chisel CB No-till C Chisel r
C Conv. C Chisel C Conv. 1
CBWM-Herb. C Conv. C Chisel-Ter. 1
CBWM-Part. CB No.-t.Ter. C Conv.-Ter. r
CB No-till CBWM-Herb. CBWM-Part. r
C Chisel-Ter. C Chisel-Ter. CBWM-Herb. 1
C Conv.-Ter. CBWM-Part. CB No-till 1
CB No-t.-Ter. C Conv.-Ter. CB No-t.Ter. r
low C No-till C No-till C No-till r
r
r
r
r
1
1
1
1
1
u
u
u
1

u
u
u
u
u

u
u

u
1

CB Chisel
CB Conv.
CB No-till *"
CB Chisel
CB Conv.
C Chisel
C Conv.
C Chisel
C Conv.
CB No-till Ter.
CBWM-Herb.
C Chisel-Ter.
CBWM-Part.
C Conv.-Ter.
C No-till ^_
C Chisel-Ter.
C Conv.-Ter.
CBWM-Part.
CBWM-Herb.
CB No-till
CB Chisel "*~
CB Conv.
C Chisel
CB No-t.-Ter. ^
•«-
C Conv.
CBWM-Herb.
CBWM-Part .
CB No-till
C Chisel-Ter.
«-
C Conv.-Ter.
CB No-t.-Ter.
•«-
C No-till
C No-till
«-
27,000

25,000




23,000



21,000



20,000




15,000



13,000





10,000


8 ,000


6,000
Notes;  C  =  corn; CB  =  corn-bean; CBWM  =  corn-bean=wheat-meadow.




        r  =  ridge; 1  =  lowlands; u  =  uplands.
                                      127

-------
                  Alternative B:   Energy Cost Increase




     Alternative B assumes a future scenario in which energy prices




have increased while other costs have remained constant.  The B Alter-




native examines the effects of this cost increase on the farmer's factors




of production and on his net return.






     Table A-5B illustrates the method used to develop the energy price




increase.  Tractor fuel cost and combine fuel cost per hour have been




increased by a factor of 2.068.  This factor was derived from the annual




price change rates for the years 1977 through 1985 for crude oil  (refiner




acquisition).  The source of these projections is Energy Review, Summer




1977, published by Data Resources, Inc., Lexington, Massachusetts.  Total




fuel cost was calculated in the same way for Table A-5B as for Table A-5.






     Table A-7B shows how fertilizer costs have been increased.  A different




price increase factor was used for each type of fertilizer depending




upon the relative amounts of different energy inputs used in its produc-




tion.  It was assumed that other inputs to the production of fertilizer




such as marketing, administration, and labor were either a very small




component of the total cost or would move proportionally to the energy




cost.  Therefore the price of the fertilizer to the farmer was assumed




to increase at the same rate as that of the energy inputs to fertilizer




production.  (This same assumption was made for pesticide costs, corn




drying costs, and fuel costs.)  Sources of the percentages of energy




inputs to fertilizer production are given in the footnotes to Table A-7B.




The energy price increase factors were developed from projections from




the same source as for the fuel increase factor, above.  Energy input







                                   126

-------
                    Table A-5B.  Fuel Costs —  Increased  Energy Cost Alternative

I tea
Tillage Practices
C Conv.
Total tractor hours 473.00
Fuel cost per trac-
tor hour, S* 5.23
Tractor fuel
cost, $
Total combine
H-1 hours
Jg Fuel cost per
coobine hour, $*
Conbine fuel
cost, $
Total fuel
cost, $
2474.75
117.50
4.05
476.26
2951.02
C Chisel
437.25
5.23
2287.70
117.50
4.05
476.26
2763.96
Notes: C * corn; CB * corn-bean; CBUH
C No-till
352.00
5.23
1841.68
117.50
4.05
476.26
2317.94
Rotations
CB Conv.
347.27
5.23
1816.92
96.25
4.20
404.07
2220.97
CB Chisel
363.00
5.23
1899.23
96.25
4.20
404.07
2303.30
CBMM
CB No-till Part. No-till
277.75
5.23
1453.20
96.25
4.20
404.07
1857.27
541.75
5.23
2,833.35
66.88
4.26
284.91
3,118.26
CBNH
No-till, Herb.
508.75
5.23
2.660.76
66.88
4.26
284.91
2.945.67
Terraces
C Conv.
473.00
5.23
2474.75
117.50
4.05
476.26
2951.02
C Chisel
437.25
5.23
2287.70
117.50
4.05
476.26
2763.96
CB No-till
277.75
5.23
1453.20
96.25
4.20
404.07
1857.27
« corn -bean-wheat-meadow .
*   For derivation see footnotes. Table A-5.  Assume 1985/1977 price ratio of 2.068, developed from annual price
change data for crude oil  (refiner acquisition) from Energy Review, Summer 1977, Data Resources Inc., Lexington,
Massachusetts.

-------
                                Table A-7B.
                                              Fertilizer Costs - Increased B»ergy Cost Alternative
U)
o

Xtea
Tillage Practices
C Conv.
C Chisel
C Ho-till
	 Rotations
CB Conv.
CB Chisel
CB No-till Pai
CBMM
-t. No-till K
CBHH


Average annual Fertilizer ' " 	 ,
aaount , Ibs/acre*
N
A uplands
B ridge
C lowlands
PjOs
A uplands
B ridge
C lowlands
KzO
125
160
160
44
40
40
5O
Cost of Fertilizer**
N
A uplands
B ridge
C lowlands
PjOs
A uplands
B ridge
C lowlands
KiO
Cost of Fertilizer
acre, $
A uplands
B ridge
C lowlands
Total cost of
Fertilizer, $
34.92
44.70
44.70
17.06
15.51
15.51
9 22
per
61.20
69.43
69.43

A uplands 15,300
B ridge 17,357.50
C lowlands 17,357.50
125
160
160
44
40
40
34.92
44.70
44.70
17.06
15.51
15.51
61.20
69.43
69.43
15,300
17,357.50
17,357.50
137.50
176
176
44
40
40
38.42
49.17
49.17
17.06
15.51
15.51
64.70
73.90
73.90
57.50
75
75
27.50
25
25
16.06
20.96
20.96
10.67
9.69
9.69
37.79
41.71
41.71
16,175 9,447.50
18,475 10,427.50
18,475 10,427.50
57.50
75
75
27.50
25
25
16.06
20.96
20.96
10.67
9.69
9.69
37.79
41.71
41.71
9,447.50
10,427.50
10,427.50
63.25
82.50
82.50
27.50
25
25
17.67
23.05
23.05
10.67
9.69
9.69
39.40
43.80
43.80
9,850
10,950
10,950
33.75
42.50
42.50
24.75
22.50
22.50
9.43
11.88
11.88
9.59
8.74
8.74
31.92
33.52
. 33.52
7,980
8,380
8,380
35.63
45.25
45.25
24.75
22.50
22.50
9.96
12.64
12.64
9.59
8.74
8.74
12.90
32.45
34.28
34.28
125
160
160
44
40
40
50
34.92
44.70
44.70
17.06
15.51
__.. 15.51
9.22
61.20
69.43
69.43
8,112.50 13,300
8,570 17,357.50
8,570 17,357.50
125
160
160
44
40
40
50
34.92
44.70
44.70
17.06
15.51
15.51
9.22
61.20
69.43
69.43
15,300
17,357.50
17,357.50
63.25
82.50
27.50
25
25
17.67
23.05
23.05
10.67
9.69
9.69
11.06
39.40
43.80
43.80
9,85O
10,950
10 , 950

-------
                                                   Table  A-7B  (continued)

Item
Tillage Practices
C Conv. C ChiMl C HO-till
Rotation*
CBHM
CB Conv. CB ChiMl CB No-till Part. No-till
CBHM
No-till, Herb.
Terraces
C Conv. C Chliel
CB No-till
Rental of appli-
cation equipment,
$*
Total Fertilizer
Costs, $
A uplands
B ridge
C lowlands
262.50
15,562.50
17,620
17,620
262.50
15,562.50
17,620
17,620
262 . 50
16,437.50
18,737.50
18,737.50
131.25
9,578.75
10,558.75
10,558.75
131.25
9,578.50
10,558.75
10,558.75
131.25
9,981.25
11,081.25
11,081.25
153.13
8,133.13
8,533.13
8,533.13
153.13
8,265.63
8,723.13
8,723.13
262.
15,562.
17,620
17,620
.50 262.50
.50 15,562.50
17,620
17,620
131.25
9,981.25
11,081.25
11,081.25
Motes:  C » cornj CB - corn-bean; CBWN * corn-bean-wheat-meadow.

•     For  derivation see  Table A-7B;  see  footnotes.  Table A-7B.

*•   Cost of fertilizer derived from  fertilizer prices from Table 7 multiplied by the following 1985/1975 price ratios:  N — 2.149;   PjOs  —
2.041; KiO — 2.048.  Fertilizer price ratios  are produced by multiplying energy input amounts  by energy input price ratios.   Energy  inputs
to N:  95% natural gas; 5% electricity  (Source:  Davis, C. H. and G. M. Blouin, "Energy Consumption  in  the U.S. Chemical Fertilizer System
from the Ground to the Ground," p.  321 in W. L. Lockertz  (ed.), Agriculture and Energy, Academic Press, New York, 1977.)  Energy inputs  to
PjO5:  18% oil, 69% natural gas. 111  electricity  (percents developed from data in White,  W.  C.  and K. T. Johnson, Energy Requirements for the
Production of Phosphate Fertilizers,  Draft,  The Fertilizer Institute, Washington, D.C. (no date)).   Energy inputs to KaO:  81* natural gas,
11% electricity  (percents developed from data  in White, W. C., "Fertilizer-Food-Energy Relationships,"  paper presented at the American Chemical
Society Division of Fertilizer  and soil Chemistry, Chicago, Illinois, August 28, 1973).   1985/1977  price ratios for natural gas (industrial),
electrictiy  (Marginal industrial),  and crude oil  (refiner acquisition) of 2.185, 1.462, and  2.068, respectively, developed from annual price
change data from Energy Review, Summer  1977, Data lesources, Inc., Lexington, Massachusetts.

-------
percentages were multiplied by  energy price  increase  factors and  then




summed to obtain the price increase  factor for each type of fertilizer.






     Pesticide cost increases are given  in Table A-8B and were calculated




in the same way as fertilizer cost increases.  All pesticide costs are




assumed to increase by the same  factor, 2.013, since the percentages of




energy inputs are assumed to be the  same for all.  The source of  this




information is listed in the footnote to Table A-8B which also lists the




energy price increase factors and their  source.






     Corn drying costs  (Table A-10B)  are increased due to increased energy




cost.  Off-farm corn drying is based on  energy from LP gas and natural




gas.  A price increase ratio of 2.127 was used for corn drying.   The first




footnote to Table  A-10B lists the sources of data from which this figure




was calculated.  Table A-10B"also" shows  increased interest costs  necessary




to support more operating capital needed to  finance the increased ferti-




lizer, pesticide and fuel expenses which the farmer encounters in this




scenario.






     Table A-12B summarizes. Lhe energy cost  increase alternative  showing




higher fuel, fertilizer, pesticide and "other" costs.  Total costs in




Table A-12B when compared with Table A-12 have increased from between §$0,000




and $30,000 or 30 to 65 percent.  These  high cost increases, of course,




affect net return drastically.  As the "net  return" figures indicate,




many options are no longer financially viable.




     Table 'A-13B  shows  how increased energy  costs have affected the ranking




of the options in terms of net revenue.  Only 11 out of 33 options produce




a positive return, and one of these  is below $1,000.  Farmers on  the up-





                                  132

-------
                                     Table  A-8B.   Pesticide Costs — Increased  Enerqy  Cost Alt*rn»nw«

Item
Tillage Practices
C Conv. C Chisel C No-till
Dotations
CBHM
CB Conv. CB Chisel CB No-till Part. No-till
CBHM
No-till, Herb.
Terraces
C Conv. C Chisel
CB No-till
CO
Ul
              CORK
              Total Herbicide and
              Insecticide Cost, $*
A uplands
B ridge
C lowlands
SOYBEAN
Total Herbicide
Cost, $•
A uplands
B ridge
C lowlands
Total Pesticide
Cost, $
A uplands
B 'ridge
C lowlands
11,484.17
10,517.93
12,455.44

11,484.17
10,517.93
12,455.44
11,484.17
10,517.93
12,455.44

11,484.17
10,517.93
12,455.44
22,716.71
21,412.28
24,015.09

22,716.71
21,412.28
24,015.09
5,676.66
5,193.54
6,071.71
3,462.36
2,632
4,252.46
9,139.02
7,825.54
10,414.76
5,676.66
5,193.54
6,071.71
3,462.36
2,632
4,252.46
9,139.02
7,825.54
10,414.76
8,442.16
7,878.38
9,015.72
3,761.79
2,891.17
4,589.64
12,203.81
10,769.55
13,605.36
3,856.
3,614.
4,098.
1,880.
1,445.
2,294.
5,737.
5,060.
6,393.
16
60
97
91
60
82
07
20
79
5,145.73
4,904.17
5,388.56
1,880.91
1,445.60
2,294.82
7,026.64
6,349.77
7,683.38
11,484.17
10,517.93
12,455.44

11,484.17
10,517.93
12,455.44
11,484.17
10,517.93
12,455.44

11,484.17
10,517.93
12,455.54
8,442.02
7,878.38
9,015.72
3,761.79
2,891.17
4,589.64
12,203.81
10,769.55
13,605.36
              Notes:  C * corn;  CB - corn-bean; CBHM « corn-bean-wheat-meadow.

              *    For derivation of pesticide amounts see Table 8 and footnotes to Table  8.   Pesticide  costs have been increased  using a 1985/1977 price
              ratio of 2.013.  This price ratio was developed by multiplying pesticide energy  input amounts by energy input price  ratios.  Energy inputs to
              the production of pesticides are 42% oil, 38% natural gas, 20% coal (Source:  Pinentel, David, Energy Inputs for the Production, Formulation,
              Packaging and Transport of Various  Pesticides, Draft, November 1977,  p.  3).   1985/1977 price ratios for crude oil (refiner acquisition),
              natural gas (industrial),  and coal  (contract) of 2.068, 2.185, and 1.568,  respectively, were developed from annual price change data from
              Energy Review, Summer 1977, Data Resources, Inc., Lexington,  Massachusetts.

-------
                             Table A-10B.
                                              Others Costs -  Energy Cost Increase Alternative
Item
Corn Drying
Tillage Practices
C Conv.

Grain harvested, bu
A uplands 26,250
B ridge 32,500
C lowlands 32,500
Cost per bu, $* 734
A uplands 8,933.40
B ridge 11,060.40
C uplands 11,060.40
Operating Capital**
Fertilizer (8 no.)***
A uplands 1,190.53
B ridge 1,347.93
C lowlands 1,347.93
Seed (8 mo.)
A uplands
B ridge
C lowlands
Pesticide (6 «o.)+
A uplands
B ridge
C lowlands
Fans -*>.)*+ 	
ijaoor (j BO.)
Total Interest
A uplands
B ridge
C lowlands
134.87
148.47
162.07
488.08
447.01
529.36
	 62.71
30.88
1,906.57
2,037.00
2,132.95
Total Other Costs
A uplands 10,839.97
B ridge 13,097.40
C lowlands 13,193.35
C Chisel
26,250
32 , 500
32,500
.34
8,933.40
11,060.40.
11,060.40
1,190.53
1,347.93
1,347.93
134.87
148.47
162.07
488.08
447.01
529.36
58.73
1,899.49
2,029.92
2,125.87
10,832.89
13,090.37
13,186.27
C No-till
24,937.50
32,500
26,000
34
8,486.73
11,060.40
8,848.32
1,257.47
1,433.42
1,433.42
141.67
155.27
168.87
965.46
910.02
1,020.64
49.26
20.37
2.434.23
2,568.34
2,692.56
10,920.96
13,628.74
11,540.88
	 Rotations
CB Conv.
13,781.25
17,062.50
17,062.50
4,690.04
5,806.71
5,806.71
732.77
807.74
807.74
143.93
150.73
157.53
388.41
332.59
442.63
47.20
21.81
1,334.12
1,360.07
1,476.91
6,024.16
7,166.78
7,283.62
CB Chisel
13,781.25
17,062.50
17,062.50
4,690.04
5,806.71
5,806.71
732.77
807.74
807.74
143.93
150.73
157.53
388.41
332.59
442.63
48.96
23.17
1,337.24
1,363.19
1,480.03
6,027.28
7,169.90
7,286.74
CB No-till Pai
13,781.25
17,062.50
15,356.25
4,690.04
5,806.71
5,226.04
763.57
847.72
847.72
151.16
157.96
164.76
518.66
457.71
578.23
J974T
15.77
1,488.63
1,518.63
1.645.95
6,178.67
7,325.34
6,871.99
CBWM
•t. No-till
7,218.75
8,937.50
8,937.50
2,456.69
3,041.61
3,041.61
622.18
652.78
652.78
163 . 34
166.74
170.14
243.83
215.06
271.74
, , ,66 -.26
17.12
1,112.73
1,117.96
1,178.04
3,569.42
4,159.57
4,219.65
CBWM


7,218.75 28,000
8,937.50 34,250
8,490.63 34,250
	 .34 	 .34
2,456.69 9,528.96
3,041.61 11,655.96
2,889.53 11.655.96
632.32
667.32
667.32
160.22
163.62
167.02
298.63
269.87
326.54
	 14.25
1,168.02
1,177.66
1,237.73
1,190.53
1,347.93
1,347.93
134.87
146.47
162.07
488.08
447.01
529.36
•6277V
30.88
1,906.57
2,037.00
2,132.95
3,624.71 11,435.53
4,219.27 13,692.96
4, 127. 26 13,788.91
28 , 000
34,250
9,528.96
11,655.96
11,655.96
1,190.53
1,347.93
1,347.93
134.87
148.47
488.08
447.01
529.36
' 58.73
27.78
1,899.49
2,029.92
2,125.87
11,428.45
13,685.88
13,781.83
14,656.25
17,937.50
4,987.82
6,104.49
5,523.82
763.57
847.72
151.16
157.96
518.66
457.71
578.23
1,488.63
1,518.63
1,645.95
6,476.45
7,623.12
7,169.77
 Notes:  C - corn; CB - corn-bean; CBWM - corn-bean-wheat-meadow.

 *    For initial price and cost derivation see Table 10.   Price and cost have been increased using a  1985/1977 price ratio of  2.127 derived
 by multiplying the energy input amounts to off-fam corn  drying (SO* LP gas and 5O% natural gas,  U.S. Food and Fiber Sector, U.S. Senate  Report,
 September 1974) by 1985/1977 price ratios for crude oil (refiner acquisition) and natural gas (industrial) of 2.068 and 2.185, respectively.
 These price ratios were developed from annual price change data in Energy Review, Summer 1977, Data Resources, Inc., Lexington, Massachusetts.

 **   See  footnotes  in Table A-10,  * "Fertilizer  costs from Table A-7B, +Pesticide  costs  from Table  A-8B,
•n-Fuel costs  from  Table A-5B.

-------
                                   Table A-12B.  Su«ary - Energy  Cost Increase Alternative

Item

Tillage
Practices
C Conv. C Chisel C No-till

A uplands 52,500 52,500
B ridge 65,000 65,000
C lowlands 65,000 65,000
Rotations
CB Conv.
49,875 46,312.50
65,000 59,125
52,000 59,125
CB Chisel
46,312.50
59,125
59,125
CB No-till Pa
44,437.50
57,875
50,712.50
CBHM
rt. No-till
43,031.25
51,781.25
49,906.25
CBHM
No-till, Herb.

C Conv.
43,031.25 56,000
51,781.25 68,500
49,012.50 68,500
Terraces
C Chisel
56,000
68 , 500
68,500

CB No-t
47,437
60,875
53,712

ill
.50
.50
Ul
en
Costs
Tractor (excl.
fuel)
Implements
(excl. fuel)
Fuel*
Seed
A uplands
B ridge
C lowlands
Fertilizer**
A uplands
B ridge
C lowlands
Pesticides***
A uplands
B ridge
C lowlands
Labor
Terracing
Other*
A uplands
B ridge
C lowlands
Total Cost (Net
Land Cost)
A uplands
B ridge
C lowlands
Net Return (Excl
Land Cost)
A uplands
B ridge
C lowlands
Notes: C - corn
4,604.91
10,643.65
2,951.02
2,380
2,620
2,860
15,562.50
17,620
17,620
11,484.17
10,517.93
12,455.44
2^149.42
0
10,839.97
13,097.40
13,193.35
of
60,615.64
64,204.33
66,477.79

-8,115.64
795.67
-1,477.79
4,537.42
10,365.66
2,763.96
2,380
2,620
2,860
15,562.50
17,620
17,620
11,484.17
10,517.93
12,455.44
2,019.30
0
10,832.89
13,090.32
13,186.27
59,945.90
63,534.59
65,808.05
-7,445.90
1,465.41
-808.05
; CB - corn-bean; CBHM
4,281.19
8,913.07
2,317.94
2,500
2,740
2,980
16,437.50
18,737.50
18,737.50
22,716.71
21,412.28
24,015.09
1,708.98
0
10,920.96
13,628.74
11,540.88
69,796.35
70,999.70
74,494.65
-19,921.35
-5,999.70
-22,494.65
4,272.26
11,134.31
2,220.97
2,540
2,660
2,780
9,578.75
10,558.75
10,558.75
9,139.02
7,825.54
10,414.76
1,691.76
0
6,024.16
7,166.78
7,283.62
46,601.23
47,530.37
50,356.43
-288.73
11,594.63
8,768.57
4,301.95
10,828.87
2,303.30
2,540
2,660
2,780
9,578.75
10,558.75
10,558.75
9,139.02
7,825.54
10,414.76
1,671.68
0
6,027.28
7,169.90
7,286.74
46,390.85
47,319.99
50,146.05
-78.35
11,805.01
8,978.95
4,056.64
9,376.28
1,857.27
2,667.50
2,787.50
2,907.50
9,981.25
11,081.25
11,081.25
12,203.81
10,769.55
13,605.36
1,361.36
0
6,178.67
7,325.34
6,871.99
47,682.78
48,615.19
51,117.65
-3,245.28
9,259.81
-405.15
4,734.71
14,493.38
3,118.26
2,882.50
2,942.50
3,002.50
8,133.13
8,533.13
8,533.13
5,737.07
5,060.20
6,393.79
2,215.42
0
3,569.42
4,159.57
4,219.65
44,883.89
45,257.17
46,710.84
-1,852.64
6,524.08
3,195.41
4,672.41
13,728.36
2,945.67
2,912.50
2,972.50
2,032.50
8,265.63
8,723.13
8,723.13
7,026.64
6,349.77
7,683.38
2,095.30
0
3.624.71
4,219.27
4,127.26
45,271.22
45,706.41
47,008.01
-2,239.97
6,074.84
2,004.49
4 , 604 . 91
10,643.65
2,951.02
2,380
2,620
2,860
15,562.50
17,620
17,620
11,484.17
10,517.93
12,455.44
2,149.42
6,460
11,435.53
13,692.96
13,788.91
67,671.20
71,259.89
73,533.35
4,537.42
10,365.66
2,763.96
2,380
2,620
2,860
15,562.50
17,620
17,620
11,484.17
10,517.93
12,455.44
2,019.30
6,460
11,428.45
13,685.88
13,781.83
67 , 001 . 46
70,590.15
72,863.61
-11,671.20-11,001.46
-2,759.89 -2,090.15
-5,033.35 -4,363.61
4,056.64
9,376.28
1,857.27
2,667.£.n
2,787.50
2,907.50
9,981.25
11,081.25
11,081.25
12,203.81
10,769.55
13,605.36
1,361.36
6,460
6,476.45
7,623.12
7,169.77
54,440.56
55,372.97
57,875.43
-7,003.06
-5, 307.83
-4,162.93
» corn-bean-wheat-aeadow.
         *Fuel costs from Table A-5B, "Fertilizer costs  from Table  A-7B,  ***Pesticide costs from Table A-8B, mother costs
         I ITOItl TcLDJ-6  A™ -

-------
                                   Table A-13B




            Net  Revenue  Ranking  — Energy Cost  Increase Alternative
        Uplands
Lowlands
All Soils
high CB Chisel CB Chisel CB Chisel r
CB Conv. CB Conv. CB Conv. r
CBWM-Part. CB No-till CBWM-Part. r

CBWM-Herb. CBWM-Part. CBWM-Herb. 1
CB No-till CBWM-Herb. CB No-till 1
CB No-t.-Ter. C Chisel C Chisel r
C Chisel C Conv. C Conv. r

C Conv. C Chisel-Ter. CB No-t.-Ter. 1
C Chisel-Ter. C Conv.-Ter C Chisel-Ter 1

C Conv.-Ter. CB No-t.-Ter. C Conv.-Ter. r
low C No-till C No-till C No-till r
u
u
1
1

1
u
r
u
r

u
1
1

1
r
r
u
u
u
u
u
u
1

CB Chisel
CB Conv.
CB No-till

CB Chisel
CB Conv.
CBWM-Part .
CBWM-Herb.

CBWM-Part
CBWM-Herb

C Chisel
C Conv
CB Chisel
CB Conv.
CB No-till
C Chisel

C Conv.
CBWM-Part.
C Chisel-Ter.
CBWM-Herb.
C Conv.-Ter.

CB No- till
CB No-t.-Ter.
C Chisel-Ter.

C Conv.-Ter.
CB No-till-Ter.
C No-till
CB No-t.-Ter.
C Chisel
C Conv.
C Chisel-Ter.
C Conv.-Ter.
C No-till
C No-till

+• 12,000


•<- 9 000
•J f W W



•*• 6 000
\J f \J\J\J

+• 2,000

•*- 1,000




«- -3,000





•*• -3,000



•*• —5,000



«• -6,000


«- -9,000

+ -12,000
«- -20,000
•*- -23,000
Notes;    C  =  corn; CB  =  corn-bean; CBWM  =  corn-bean-wheat-meadow.




         r  =  ridge; 1  =  lowlands; u  =  uplands.
                                        136

-------
lands no longer have revenue producing options available to them.  The




CBWM options are the least energy intensive and their costs increase the




least so they move up in rank for all soil types.  On the uplands, they




move from seventh and ninth place to third and fourth place.  They also




rank high on the other two soil types moving from tenth and eleventh




place to fourth and fifth place on the ridge and from eighth and ninth




place to third and fourth place on the lowlands when compared to the base




case (Table A-13 ).





     Revenue from the corn-soybean rotations, chisel and conventionally




tilled on the ridge and lowlands, is high and their use of energy intensive




factors of production such as fertilizer and pesticides is relatively low




compared to continuous corn, for example, so that these options remain




the most attractive financially.  This is also true for the corn-soybean




no-tillage rotation on the ridge soil.  In contrast, the continuous corn




options, both conventionally and chisel tilled, use relatively more of the




energy intensive factors of production, enough to negate the effect of




their high gross revenues.  The energy price increase in this instance




serves to highlight the natural benefits provided by the soybeans to the




corn in the form of pest control and nitrogen fertilizer credit.






     Table  A-15B  shows  the  effects  of  combining  the energy  price  increase




future scenario with alternative A, the use of custom hiring to carry out




certain operations in the corn-soybean-wheat-hay rotation options.  The




costs and revenues for the two options displayed in this table offer perhaps




a more realistic picture of the effect of a large energy price increase.




Both options become relatively more attractive financially  in comparison
                                    137

-------
Table A-15B.
CBWH Farm Practice with Custom Rate
and 1985 Energy Prices


Tractor
Implements
A
B
C
Fuel
Seed
A
B
C
Fertilizer
A
B
C
Biocides
A
B
C
Labor
Drying & Intr'
A
B
C
Total Cost
A
B
C
Gross Revenue
B
C
Net Revenue
A
B
C

Custom
Option
3,942
13,432
13,659
13,659
687












1,034
t
1,773
2,051
2,083







12,429
20,770
18,141
1977
Non
Custom
4,735
14,493
14,493
14,493
1,508

2,882
2,942
3,002

4,001
4,181
4,181

2,850
2,514
3,176
2,215
1,795
2,073
2,104

34,480
34,661
35,415



8,552
17,120
14,491

R77=
Custom
Non Custom
.833
.927
.943
.943
.456

1.0
1.0
1.0

1.0
1.0
1.0

1.0
1.0
1.0
.467
.988
.989
.990









1985
Non
Custom
4,735
14,493
14,493
14,493
3,118

2,882
2,942
3,002

8,133
8,533
8,533

5,737
5,060
6,394
2,215
3,569
4,160
4,220

44,884
45,257
46,711
43,031
51,781
49,906
-1,852
+6,524
+3,195

Custom = R-
x Non
Customg 5
3,942
13,432
13,659
13,659
1,422

2,882
2,942
3,002

8,133
8,533
8,533

5,737
5,060
6,394
1,034
3,526
4,114
4,174

42,985
43,583
45,037
43,031
51,781
49,906
+46
+8,198
+4,869
                        138

-------
Table A-15B.  Continued.


Tractor
Implements
A
B
C
Fuel
Seed
A
B
C
Fertilizer
A
B
C
Biocides
A
B
C
Labor
Drying & Intr
A
B
C
Total Cost
A
B
C
Gross Revenue
A
B
C
Net Revenue
A
B
C

Custom
Option
3,809

12,667
12,894
12,894
611


No
change


No
Change


No
Change
924
•t
1,798
2,078
2,038

30,273
30,711
31,393





12,759
21,071
17,619
1977
Non
Custom
4,672

13,728
13,728
13,728
1,425

2,912
2,972
3,032

4,061
4,268
4,268

3,491
3,154
3,817
2,095

1,819
2,099
2,059

34,203
34,415
35,097

43,031
51,781
49,012

8,828
17,367
13,915
1985
R77=
Custom
Non Custom
.815

.923
.939
.939
.429

)
jl.O
\

\
[l.O
1


1.0

.441

.989
.990
.990












Non
Custom
4,672

13,728
13,728
13,728
2,946

2,912
2,972
2,032

8,265
8,723
8,723

7,027
6,350
7,683
2,095

3,625
4,219
4,127

45,271
45,706
47,008

43,031
51,781
49,012

- 2,240
-1- 6,075
2,004
Custom = R77
x Non
Customs 5
3,809

12,667
12,894
12,894
1,263

2,912
2,972
2,032

8,266
8,723
8,723

7,027
6,350
7,683
924

3,585
4,177
4,086

40,453
41,112
41,414

43,031
51,781
49,012

+ 2,578
+ 10,669
+ 7,598
           139

-------
to other practices.  The upland farmer, for example, could use the CBWT1




no-till option to produce a positive net return.






     It can be concluded from this example that a large energy price




increase would have severe consequences to farmers causing them to switch




to farming practices which are less energy intensive, to relocate or remove




land from production, and to increase use of natural rather than manufac-




tured means of adding nutrients to the soil and of pest control.  Note,




however, that the results of this alternative are extreme, and in reality




an energy price increase such as this would have other effects on other




costs and on food prices so that the results would be somewhat different




than those of the simplified case considered here.  But this case does




serve to illustrate the direction of the effects of a large energy price




increase.
                                   140

-------
                       Alternative C:  Price Subsidy



      Alternative C examines the effect of a price subsidy policy for




 wheat.  Tables A-3 and A-14 show that although the wheat-hay rotations




 produce relatively little soil loss compared to other options,  they are




 not as attractive in terms of revenue as the continuous corn or the corn-




 soybean rotations.   In Alternative C, a price subsidy mechanism was




 used to make the wheat-hay rotation options more attractive compared to the




 highest net revenue producing options in the initial  case.   The corn-




 soybean rotation was  already more  financially appealing than the continuous




 corn option (see Table A-12), so it was not considered useful to examine a



 soybean price subsidy.







      Table A-11C shows the price of wheat subsidized to $5.00 (a subsidy




 of  $2.50 per acre)  which  doubles the  gross  revenue  from the acres planted




 with wheat  in the wheat-hay  rotation  options.  The  total  gross  revenue




 from these  options  is  thus increased  by  about  $7,000 or 15  percent.




 The  wheat/corn price ratio has been changed  from  1.25  to  2.5 and the




 wheat/soybean price ratio from 0.5 to 1.0.






     Table A-12C shows a relatively higher net return  for the two wheat-




 hay rotation options compared to the  initial case (compare with Table A-12).




Table S-13C indicates how this increased net return  has shifted  the  net




revenue ranking of the CBWM options when compared to Table A-13,"Net




Revenue Ranking.  For the uplands they have moved from seventh and ninth




 place  to first and  second, for the ridge from  tenth and eleventh to




 fourth and  fifth and for  the lowlands from eighth and  ninth to  fifth




 and  sixth.   The ranking  for all soils shows that the  highest revenue
                                    141

-------
                                 Table  A-llc.   Revenue - Price Subsidy Alternative
 Item
                         Tillage Practices
                   C Corw.   C Chisel  C Mo-till
                                                                      Rotations
                                 CBHM          CBWM
CB Conv.  CB Oii»«l  CB No-till  Part. Mo-till  no-till. Herb.
 Corn
                                                                                                                Terraces
                                                                                                        C Conv.  C Chisel  Cfl No-till
Gross Revenue, $*
A uplands 52,500
B ridge 65,000
C lowlands 65,000
Soybeans
Gross Revenue, $*
A uplands
B ridge
C lowlands
Wheat
Expected yield, bu/acre
Gross Revenue. $ 	
52,500 49,875 27,562.50
65,000 65,000 34,125
65,000 52.000 34,125
18,750
25,000
25,000


27,562.50
34,125
34,125
18,750
25,000
22,500


27,562.50
34,125
30,712.50
16,875
23,750
20,000


14,437.50
17,875
17,875
8,437.50
11,875
10,000

14,062.50
14,437.50 56,000 56,000 29,312.50
17,875 68,500 68,500 35,875
8,437.50 18,125
11,875 25,000
10,000 21 250

14,062.50
Hay.
Gross Revenue, $*
A uplands
B ridge
C lowlands
13,125
15,000
13,125
15,000
TOTAL GROSS ' ' 	 	
REVENUE, $
A
B
C
Not
uplands
ridge
lowlands

52 , 500
65,000
65,000
:orn; CB = corn-
52,500
65,000
65,000
•bean; CBWM = c
49,875
65,000
52,000
:orn-bean->
46,
59,
59,
whe«
312.50
125
125

46,
59,
59,

312.50
125
125

44
57

,437.50 50,062.50
,875 58,812.50
,712.50 '56,937.50

50,062.50 56,000 56,000 47,437.50
58,812.50 68,500 68,500 60,875

    Derivation shown in  Table A-ll,  also see footnotes,  Table A-ll
    Assumes wheat price subsidized to S5.00 per bushel.

-------
                               Table A-12C.   Sunmary — Price Subsidy Alternative

Item
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CBHM CMM
CB Conv. CB Chisel CB No-till Part. No-till* No-till, Herb.*
Terraces
C Conv. C Chisel
CB No-till
Gross Revenue, $
A uplands
B ridge
C lowlands
52 , 500
65,000
65,000
52,500
65,000
65,000
49,875
65,000
52,000
46,312.50
59,125
59,125
46,312.50
59,125
59,125
44,437.50
57,875
50,712.50
50,062.50
58,812.50
56,937.50
50,062.50
58,812.50
56,043.75
56 , OOO
68,500
68,500
56, OOO
68,500
68,500
47,437.50
60,875
53,712.50
Total Cost
(Net of Land Cost) * *
A uplands
B ridge
C lowlands
Net Return
Land Costs)
A uplands
B ridge
C lowlands
39,665.31
41,438.49
42,696.50
(Excluding
12,834.69
23,561.51
22,303.50
39,094.24
40,867.42
42,125.43
13,405.76
24,132.58
22,874.57
43,020.05
44,949.22
45,510.76
6,854.95
20,050.78
6,489.24
32,853.95
33,307.01
34,774.97
13,458.55
25,817.99
24,350.03
32,600.14
33,053.45
34,521.16
13,712.36
26,071.55
24,60,3.84
32,285.35
32,740.69
34,063.11
12,152.15
25,134.31
16.649.39
34,479.74
34,661.36
35,415.43
15 , 582 . 76
24,151.14
21,522.07
34,203.48
34,414.71
35.097.27
15,859.02
24,397.79
20,946.48
46,405.31
48,178.49
49,436.50
9,594.69
20,321.51
19,063.50
45,834.24
47,607.42
48,865.43
10,165.76
20,892.58
19,634.57
38,385.35
39,340.69
40,663.11
8,552.15
21,534.31
13.049.39
Notes:  C - corn; CB - corn-bean;  CBHM - corn-bean-vheat-Beadov.



*    Increased revenue frost Table A-11C.
     Derivation shown in Table A-12.

-------
                                   Table  A-13C




                 Net Revenue Ranking — Price Subsidy Alternative
         Uplands
Lowlands
All soils
high C BWM-Herb. CB Chisel CB Chisel
CBWM-Part. CB Conv. CB Conv.
CB Chisel CB No-till C Chisel
CB Conv. CBWM-Herb C Conv.
C Chisel CBWM-Part. CBWM-Part.
C Conv. C Chisel CBWM-Herb.
CB No-till C Conv. C Chisel-Ter.
C Chisel-Ter. CB No-t.-Ter. C Conv.-Ter.
C Conv.-Ter. C Chisel-Ter. CB No-till
CB No-t.-Ter. C Conv.-Ter. CB No-t.Ter.
low C No-till C No-till C No-till























r
r
r
1
r
r
r
1
1
r
1

1
r
r
1
1
1
u
u

u
u
u
1

u
u
u
u
u
u
1

CB Chisel
CB Conv.
CB No-till
CB Chisel
CBWM-Herb .
C Chisel
C Conv.
C Chisel
C Conv.
CB No-till-Ter.
CBWM-Part .

CBWM-Herb.
C Conv.-Ter.
C No-till
C Chisel-Ter.
C Conv.-Ter.
CB No-till
CBWM-Herb .
CBWM-Part.

CB Chisel
CB Conv.
C Chisel
CB No-till.-Ter.

C. Conv.
CB No-till
C Chisel-Ter.
C Conv.-Ter.
CB No-till Ter.
C No-till
C No-till

27,000


25,000



23,000



21 ,000



20,000




15,000




13 000
J. -J f \J\S\S


10,000

8,000

6,000
Notes;   C  =  corn; CB  =  corn-bean; CBWM  =  corn-bean-wheat-meadow.




         r  =  ridge; 1  =  lowlands; u  =  uplands.
                                      144

-------
producing CBWM option (on ridge soils) has moved from sixteenth to




fifth place.  If the results of the Custom Wheat Hay Alternative (A)




were combined  with a wheat price subsidy (Alternative C) then the CBWM




options would become even more attractive financially.  It can be con-




cluded, then, that a price subsidy policy could be effective in encour-




aging the use of cropping patterns which have different water quality




impacts than those that would otherwise be used.
                                  145

-------
                     Alternative D:  Fertilizer Tax




     The objective of  this  alternative  is  to  illustrate  the  effects




 of  a tax on  the use of nitrogen  fertilizer.   Such  a  tax  policy might




 be  considered  to  control  level of  nitrates in public drinking water




 to  meet Federal standards.






     In Table  A-7D a $.07 tax per  pound of nitrogen  fertilizer was assumed,




 raising the  cost  from  $.13  a  pound to $.20 a  pound.   This is a substantial




 price increase.   Comparing  "cost of fertilizer per acre" and "total




 cost of fertilizer" in Table  A-7D  with  the same  row  in Table A-7, the




 effect of the  tax has  been  to rai.se fertilizer expenses  by about 35 per-




 cent for the option using the most nitrogen fertilizer and by about




 15  percent for the option using  the least.  Table  A-10D' simply carries




 through the  impact of  the increased fertilizer costs  from Table A-7D




 on  interest  costs (compare  with  Table A-10).






     Table "A-12-D  summarizes the  changes du'e to the fertilizer tax,




 including increased fertilizer and interest (other)  costs.   A compari-




 son with Table A-12 shows that net 'return  ha's been significantly decreased,




by $3300 for the options  using most nitrogen  and by about $800 for the options




using least nitrogen.  This is a reduction in  net  return of  50 percent




for the continuous corn,  no-tillage option on  the  lowlands.






     Table T\-13D  when  compared with Table  A-13,  Net  Revenue  Ranking,




indicates how the fertilizer  tax has shifted the financial return




positions of the various  fanning options.  The CBWM  options, those using




the least amount of nitrogen  fertilizer, have  moved  up in the ranking for




the upland soils.  The ranking of  the continuous corn, no-tillage options,





                                    146

-------
                           Table A-7D.   Fertilizer Costs  — Fertilizer Tax Alternative
Itom
Tillage Practices
C Cony, C Chisel C No-till
Rotations
CB Cony,
CB Chisel
CBNN
CB No-till Part. No-till
CBMH
NQ-tiU. Herb,
Terraces
CConv.
C Chisel
CB No-till
Average Annual Fertilizer
amount, Ibs/acre*
N
A uplands
B ridge
C lowlands
PjOS
A uplands
B ridge
C lowlands
KjO
Cost of Fertilizer
per acre, $••
A uplands
B ridge
C lowlands
125
160
160
44
40
40
50
37.86
44.10
44.10
125
160
160
44
40
40
50
37.86
44.10
44.10
137.50
176
176
44
40
40
50
40.36
47.30
47.30
57.50
75
75
27.50
25
25
60
22.13
25.15
25.15
57.50
75
75
27.50
25
25
60
22.13
25.15
25.15
63.25
82.50
82.50
27.50
25
25
60
23.28
26.65
26.65
33.75
42.50
42.50
24.75
22.50
22.50
70
17.75
19.08
19.08
35.63
45.25
45.25
24.75
22.50
22.50
70
18.13
19.63
19.63
125
160
160
44
40
40
50
37.86
44.10
44.10
125
160
160
44
40
40
50
37.86
44.10
44.10
63.25
82.50
82.50
27.50
25
25
60
23.28
26.65
26.65
Total Cost of Ferti-
lizer, $
A uplands
B ridge
C lowlands
9,465
11,025
11,025
Rental of Application
Equipment, 5* 262.50
Total Fertilizer
Costs, $
A uplands
B ridge
C lowlands
Notes: C » corn;
9,727.50
11,287.50
11,287.50
9,465
11,025
11,025
262 . 50
9,727.50
11,287.50
11,287.50
CB m corn-bean; CBHM
10,090
11,825
11,825
262 . 50
10,352.50
12,087.50
12,087.50
5,532.50 5,
6,287.50 6,
6,287.50 6,
131.25
5,663.75 5,
6,237.50 6,
6,237.50 6,
532.50
287.50
287.50
131.25
663.75
237. 5C
237.50
5,820
6,662.50
6,662.50
131.25
5,951.25
6,793.75
6,793.75
4,437.50
4,770
4,770
153.13
4,590.63
4,923.13
4,932.13
4,532.50
4,907.50
4,907.50
153.13
4,685.63
5 , 060 . 63
5,060.63
9,465
11,025
11,025
262 . 50
9,727.50
11,287.50
11,287.50
9,465
11,025
11,025
262 . 50
9,727.50
11,287.50
11,287.50
5,820
6,662.50
6,662.50
131.25
5,951.25
6,793.75
6,793.75
= corn-bean-wheat-meadow.
     Derivation shown in Table  A-7,  See  footnotes Table A-7.

     Assume prices per Ib. are  SO.20 for N  ($.07 tax), SO.19  for PzOs,  and SO.09  for KzO.

-------
                                 Table  A-10D.
                                                Other Costs ~ Fertilizer Tax Alternative
00
XteB
Corn Drying
Total Cost*
A uplands
B ridge
C lowlands
Capital*
Fertiliser (8 mo.)
A uplands
B ridge
C lowlands
A uplands
B ridge
C lowlands
A uplands
B ridge
C lowlands
Total Interest
A uplands
B ridge
C uplands
Total Other Costs
A uplands
B ridge
C lowlands
Tillage Practices
C Conv. c Chisel
4,200 4,200
5,200 5,200
5,200 5,200

k*
744.15 744.15
863.49 863.49
863.49 863.49
134.87 134.87
148.47 148.47
162.07 162.07
242.46 242.46
222.06 222. O6
262.97 262.97
30.32 28.40
30.88 27.78
1,182.68 1,177.66
1,295.22 1,290.20
1,350.73 1,345.71
5,382.68 5,377.66
6,495.22 6,490.20
6,550.73 6,545.71
Motes; C - cornj CB - corn -bean; CBHM -
* Derivation shown in Table A-10,
** Fertilizer costs frost Table A-7D.
C No-till
3,990
5,200
4,160
791.97
924.69
924.69
141.67
155.27
168.87
479.61
452.09
507.03
23.82
20.37
1,457.44
1,576.24
1,644.78
5,447.44
6,776.24
5,8O4.78


2,205 2
2,730 2
2,730 2
433.28
484.32
484 . 32
143.93
150.73
157.53
192.95
165.22
219.88
22.82
21.81
814.79
844.90
912.36
3,019.79 3,
3,574.90 3,
3,642.36 3,

Chisel
,205
,730
,730
433.28
484.32
484.32
143.93
150.73
157.53
192.95
165.22
219.88
23.67
23.17
817
847.11
914.57
022
577.11
644.57
Hotati
CB No-till
2,205
2,730
2,457
455.27
519.72
519.72
151.16
157.96
164.76
257.66
227.38
287.25
19.08
	 15.77
898 . 94
939.91
3,103.94
3,669.91
3,463.58
corn-bean-wheat -Meadow .
see footnotes Table A-10 .
ons
CBMN
Part. No-till
1,155
1,430
1,430
351.18
376.62
376.62
163.34
166.74
170.14
121.13
1O6.83
134.99
32.05
	 17.12
684.82
699.36
730.92
1,839.82
2,129.36
2,160.92


CBWM
No-till. Herb.
1,155
1,430
358.45
387 . 14
160.22
163.62
148.35
134.06
30.29
14.25
711.55
729.35
760.91
1,866.55
2,159.35
2,119.41


C Conv.
4,480
5,480
744.15
863.49
134.87
148.47
242.46
222.06
30.32
30.88
1,182.68
1,295.22
5,662.68
6,775.22
6,830.73


C Chisel
4,430
5,480
744.15
863.49
134.87
148.47
242.46
222.06
28.40
27.78
1,177.66
1,290.20
5,657.66
6,770.20
6,825.71

	 1

2,345
2,870
455.27
591 . 72
151.16
157.96
257.66
227.38
19 08
15.77
898.94
939.91
3,243.99
3,809.91
3,603 58


-------
                                  Table A-12D.   S
y — Fertilizer Tax Alternative

Item
Gross Revenue, $

C Conv

A uplands 52,500
B ridge 65,000
C lowlands 65,000
Tillage Practices
C Chisel
52,500
65,000
65,000
C No-till
Rotations
CB Conv.
49,875 46,312.50
65,000 59,125
52,000 59,125
CB Chisel
46,312.50
59,125
59,125
CB No-till
44,437.50
57,875
50,712.50
CBNM
Part. No-till
43,031.25
51,781.25
49,906.25
CBHN
No-till, Herb.
Terraces
C Conv.
43,031.25 56,000
51,781.25 68,500
49,012.50 68,500
C Chisel
56,000
68,500
68 , 500
CB No-till
47,437.50
60,875
53,712.50
ID
Costs
Tractor (excl.
fuel)
Inplenents
(excl. fuel)
Fuel
Seed
A uplands
B ridge
C lowlands
Fertilizer*
A uplands
B ridge
C lowlands
Pesticides
A uplands
B ridge
C lowlands
Labor
Terracing
Other**
A uplands
B ridge
C lowlands
Total Cost (Net of
Land Cost)
A uplands
B ridge
C lowlands
Net Return (Excl.
Land Cost)
A uplands
B ridge
C lowlands
Notes: C » corn;
4,604.91
10,643.65
1,426.99
2,380
2,620
2,860
9,727.50
11,287.50
11,287.50
5,705
5,225
6,187.50
2,149.42
0
5,382.68
6,495.22
6,550.73
42,020.15
44,452.69
45,710.70
10,479.85
20,547.31
19,289.30
4,537.42
10,365.66
1,336.54
2,380
2,620
2,860
9,727.50
11,287.50
11,287.50
5,705
5,225
6,187.50
2,019.30
0
5,377.66
6,490.20
6,545.71
41,449.40
43,881.62
45,139.63
11,050.60
21,118.38
19,860.37
CB - corn-bean; CBHM
4,281.19
8,913.07
1,120.86
2,500
2,740
2,980
10,352.50
12,087.50
12,087.50
11,285
10,637.50
11,930
1,708.98
0
5,447.44
6,776.24
5,804.78
45,609.04
48,264.84
48,826.38
4,265.96
16,735.16
3,173.62
4,272.26
11,134.31
1,073.97
2,540
2,660
2,780
5,663.75
6,237.50
6,237.50
4,540
3,887.50
5,173.75
1,691.76
0
3,019.79
3,574.90
3,642.36
33,935.84
37,531.95
36,005.91
12,376.66
21,593.02
23,119.09
4,301.95
10,828.87
1,113.78
2,540
2,660
2,780
5,663.75
6,237.50
6,237.50
4,540
3,887.50
5,173.75
1,671.68
0
3,022
3,577.11
3,644.57
33,682.03
34,278.39
35,752.10
12,630.47
24,846.61
23,372.90
4,056.64
9,376.28
898.10
2,667.50
2, 787. SO
2 , 907 . 50
5,951.25
6,793.75
6,793.75
6,062.50
5,350
6,758.75
1,361.36
0
3,103.94
3,669.91
3,463.58
33,477.57
34,293.54
35,615.96
10,959.93
23,581.46
15,096.54
4.734.7X
14,493.38
1.508.40
2,882.50
2,942.50
3,002.50
4,590.63
4,923.13
4,923.13
2,850.01
2,513.76
3,176.25
2,215.42
0
1,839.82
2,129.36
2,160.92
35,114.87
35,496.66
36,214.73
7,916.38
16,284.59
13,691.52
4,672.41
13,728.36
1.429.91
2,912.50
2,972.50
3,032.50
4,685.63
5,060.63
5,060.63
3,490.63
3,154.38
3,816.88
2,095.30
0
1,866.55
2,159.35
2,119.41
34,881.29
35,272.84
35,955.40
8,149.96
16,508.41
13,057.10
4,604.91
10,643.65
1,426.99
2,380
2,620
2,860
9,727.50
11,287.50
11,287.50
5,705
5,225
6,187.50
2,149.42
6,460
5,662.68
6,775.22
fc,830.73
48,760.15
51,192.69
52,450.70
7,239.85
17,307.31
16,049.30
- corn-bean-wheat-meadow.
4,537.42
10,365.66
1,336.54
2,380
2,620
2,860
9,727.50
11,287.50
11,287.50
5,705
5,225
6,187.50
2,019.30
6,460
5,657.66
6,770.20
6,825.71
48,189.08
50,621.62
51,879.63
7,810.92
17,878.38
16,620.37

4,056.64
9,376.28
898.10
2,667.50
2,787.50
2,907.50
5,951.25
6,793.75
6,793.75
6,062.50
5,350
6,758.75
1,361.36
6,460
3,243.94
3,809.91
3,603.58
40.077.57
40,943.54
42,215.96
7,359.93
19,931.46
11,496.54

              Fertilizer costs from Table A-7D,  **Other costs  from Table  A-10D.

-------
which use the most nitrogen, is not affected on any of the soil types since




the net return for these options was so low in the base case.  The rankings




of the options with the highest net returns, corn-soybean rotation and




continuous corn using chisel and conventional tillage, are not greatly




affected by the fertilizer tax even though these options are heavy users




of nitrogen.  The level of revenue returned to these options is lowered




slightly, however.  Overall, it can be concluded that not much change has




been affected by the tax.





     What is found from this comparison is that, in general, nitrogen




fertilizer costs are not that great relative to other expenses which the




farmer incurs, and therefore a nitrogen fertilizer tax, unless it is




extremely large, will not affect net revenue enough to cause a fanner to




switch farming practices.  Fertilizer costs range from about 12 percent




to 20 percent of the total costs that have been calculated for the




farming practice options considered.  Nitrogen costs make up 30 to 65




percent of total fertilizer costs, depending on the option considered.




Since nitrogen fertilizer costs are so small a factor, a tax such as the




one considered here will not have a significant impact.  If the tax were




imposed after an energy cost increase had occurred, however, such as that




considered in Alternative B, then a greater impact might be observed.





     Unfortunately, the example case is not flexible enough as it stands




to account for the most realistic farmer response to a tax such as the




one considered in Alternative D.  Rather than switch  tillage or  rotation




options' in response to net revenue  charges, as hypothesized here, a




farmer most probably would change his method of nitrogen fertilizer
                                    150

-------
    application to increase the use of nitrogen as a side dressing.  This




    response would tend to decrease the amount of nitrogen used while main-




    taining generally the same rotation and tillage practices.
             Table A-13D.  Net Revenue Ranking—Fertiliser Tax Alternative
Uplands
high CB Chisel
CB Conv.
C Chisel
CB No-till
C Conv.
CBWM-Herb .
CBWM-Part.
C Chisel-Ter.
CB No-t.-Ter.
C Conv.-Ter.
low C No-till


























Ridge Lowlands
CB Chisel CB Chisel r
CB No-till CB Conv. 1
CB Conv. C Chisel 1
C Chisel C Conv. r
C Conv. C Chisel-Ter. r
CB No-t.-Ter. C Conv.-Ter. r
C Chisel-Ter. CB No-till r
C Conv.-Ter. CBWM-Part. r
C No-till CBWM-Herb. 1
CBWM-Herb. CB No-t.-Ter. 1
CBWM-Part. C No-till r
r
r
1
r
r
1
1
1
1
u
u
1
u

u
u
u

u
u
u
u

u
1

All Soils
CB Chisel
CB Chisel
CB Conv.
CB No- till
CB Conv.
C Chisel
C Conv.
CB No-t.-Ter.
C Chisel
C Conv.
C Chisel-Ter.
C Conv.-Ter.
C No-till
C Chisel-Ter.
CBWM-Herb.
CBWM-Part.
C Conv.-Ter
CB No-till
CBWM-Part .
CBWM-Herb.
CB Chisel
CB Conv.
CB No-t.-Ter.
C Chisel

CB no-till
C Conv.
CBWM-Herb .

CBWM-Part .
C Chisel-Ter.
CB No-t.-Ter.
C Conv.-Ter.

C No-till
C No-till


•*- 26,000


«- 24,000



«- 20,000


•*- 19,000

<- 17,000







•*• 13,000



•*- 11,000



•*• 8,000




•*- 7,000


«- 3,000
Notes;  C  =  corn; CB  =  corn-bean; CBWM  =  corn-bean-wheat-meadow.




        r  =  ridge; 1  =  lowlands; u  =  uplands.







                                        151

-------
                  Alternative E:  Insecticide Scouting




     This alternative is based on the premise that the amount of insecti-




cides used on corn can be reduced by scouting to determine areas with




high potential soil insect problems and by treating only those fields that




need treatment with the full recommended dosage.  Other areas would not




be treated for these pests.  Alternative E shows the effects on net




revenue of such a reduced pesticide program on a typical farm in the




case study area.






     Table A-8E gives pesticide costs under the scouting alternative.




Insecticide costs per acre for corn are determined in the same manner




as for Table A-8.  The number of acres treated are based on approximate




percentages (listed in the footnote to Table A-8E) that might apply to a




typical farm on the soils and for the crop rotations under consideration.




Scouting costs are based on an assumed $2.00 per acre cost for the number




of acres that would typically need scouting for the soil types being




considered.  The lowlands, for example, are wetter and therefore more




likely to harbor certain insects.  Herbicides applied to corn are not




affected by the scouting option, nor are soybean pesticide costs since




no insecticides were applied to soybeans in the base case.  The compari-




son of "Total Pesticide Cost" in Table 8E with that in Table A-8 shows that




the scouting option has reduced pesticide costs by anywhere from $800 to




$2,250 and 12 to 40 percent depending on the farming practice used.




     Table  A-10E shows slightly reduced interest costs compared to Table A-10




in response to the reduced pesticide costs under the scouting alternative.




The reduced pesticide and interest costs are summarized in Table   A-12E along




with other costs which are the same as for the base case.  Note that




                                   152

-------
                                      Table A-8E.   Pesticide  Costs —  Insectivide Scouting Alternative
cn
Item
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CBWM
CB Conv. CB Chisel CB No-till Part. No-till
CBWM
No-till, Herb.
Terraces
C Conv. C Chisel
CD NO-tlll
Corn, Cost
Herbicide, S/acre*
A uplands
B ridge
C lowlands
Acres
Herbicide Cost, $
A uplands
B ridge
C lowlands
Insecticide , $/acre*
Acres treated**
A uplands
B ridge
C lowlands
Insecticide cost, $
A uplands
B ridge
C lowlands
Scouting Cost/acre,
Acres Scouted
A uplands
B ridge
C lowlands
Total Scouting Cost,
A uplands
B ridge
C lowlands
Total Cost, $
A uplands
B ridge
C lowlands
Soybeans, Cost
Total Cost, $*
A uplands
B ridge
C lowlands
Total Pesticide
Cost, $
A uplands
B ridge
C lowlands
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860
9.31
100
100
100
931
931
931
$ 2.00
250
250
250
$
500
500
500
4,808.50
4,328.50
5,291

4,808.50
4,328.50
5,291
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860
9.31
100
100
100
931
931
931
2.00
250
250
250
500
500
500
4,808.50
4,328.50
5,291

4,808.50
4,328.50
5,291
Notes: C - corn; CB - corn-bean; CBWM =
26.78
24.19
29.36
250
6,695
6,047.50
7,340
18.36
100
100
100
1,836
1,836
1,836
2.00
250
250
250
500
500
500
9,031
8,383.50
9,676

9,031
8,383.50
9,676
13.51
11.59
15.44
125
1,688.75
1,448.75
1,930
9.05
1.25
1.25
6.25
11.31
11.31
56.56
2.00
25
25
125
50
50
250
1,750.06
1,510.06
2,236.56
1,720
1,307.50
2,112.50
3,470.06
2,817.56
4,349.06
13.51
11.59
15.44
125
1,688.75
1,448.75
1,930
9.05
1.25
1.25
6.25
11.31
11.31
56.56
2.00
25
25
125
50
50
250
1,750.06
1,510.06
2,236.56
1,720
1 , 307 . 50
2,112.50
3,470.06
2,817.56
4,349.06
24.50
22.26
26.78
125
3 , 062 . 50
2,782.50
3,347.50
9.05
1.25
1.25
6.25
11.31
11.31
56.56
2.00
25
25
125
50
50
250
3,123.81
2,843.81
3,654.06
1,868.75
1,436.25
2,280
4,992.56
4,280.06
5,934.06
13.51
11.59
15.44
62.5
844 . 38
724.38
965
17.14
0.63
0.63
4.69
10.80
10.80
80.39
2.00
12.50
12.50
31.25
25
25
62.50
880.18
760.18
1,107.89
934.38
718.13
1,140
1,814.56
1,478.31
2,247.89
23.76
21.84
25.69
62.5
1,485
1,365
1,605.63
17.14
0.63
0.63
4.69
10.80
10.80
80.39
2.00
12.50
12.50
31.25
25
25
62.50
1,520.80
1,400.80
1.748.52
934.38
718.13
1,140
2,455.18
2,118.93
2,888.52
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860
9.31
100
100
100
931
931
931
2.00
250
250
250
500
500
500
4,808.50
4,328.50
5,291

4,808.50
4,328.50
5,291
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860
9.31
100
100
100
931
931
931
2.00
250
250
250
500
500
500
4,808.50
4,328.50
5,291

4,808.50
4,328.50
5,291
24.50
22.26
26.78
125
3,062.50
2,782.50
3,347.50
9.05
1.25
1.25
6.25
11.31
11.31
56.56
2.00
25
25
125
50
SO
250
3,123.81
2,843.81
3,654.06
1,868.75
1,436.25
2,280
4,992.56
4,280.06
5,934.06
corn-bean-wheat-meadow .
                  see Table A-8 for  derivation; see .footnotes,  Table A-8.
             **   Assumes 40% continuous corn treated; 7.5% lowlands CB and CBWM treated; 1% uplands, ridge CB and CBWM treated, based upon discussions
             with Dr.  Thomas Turpin,  Purdue University and on Turpin, F.  T., "Insect Insurance:  -Potential Management Tool  for Corn Insects," in
             Bulletin  of the Entomological Society of America, Vol. 23, No. 3, pp.  181-184, September 1977.

-------
                                  Table A-10E.
Ul
Other Costs — Insecticide Scouting Alternative

Item
Tillage Practices
C Conv.
Corn Drying
Total Cost*
A uplands 4,200
B ridge 5,200
C lowlands 5,200
Capital*
Fertilizer (8 no.)
A uplands 576.81
B ridge 649.29
C lowlands 649.29
A uplands
B ridge
C lowlands
A uplands
B ridge
C lowlands
A uplands
B ridge
C lowlands
Total Other Costs
A uplands
B ridge
C lowlands
134.87
148.47
162 . 07
204 . 36
183.96
224.87
977.24
1,042.92
1,098.43
5,177.24
6,242.92
6,298.53
C Chisel
4,200
5,200
5,200
576.81
649.29
649.29
134.87
148.47
162.07
204.36
183.96
224.87
972.22
1,037.90
1,093.41
5,172.22
6,237.91
6,293.41
C No-till
3,990
5,200
4,160
607.98
689.07
689. O7
141.67
155.27
168.87
383.82
356.30
411.23
23.82
1,177.66
1,244.83
1,313.36
5,167.66
6,444.83
5,473.36
	 . Rotations
CB Conv.
2,205
2,730
2,730
356.39
390.63
390.63
143.93
150.73
157.53
147.48
119.74
184.84
22.82
21.81
692.43
705.73
777.63
2,897.43
3,435.73
3,507.63
CB Chisel
2,205
2,730
2,730
356.39
390.63
390.63
143.93
150.73
157.53
147.48
119.74
184.84
23.67
23.17
694.64
707.94
779.84
2,899.64
3,437.94
3,509.84
CB No-till Pa
2,205
2,730
2,457
370.55
409.37
409.37
151.16
157.96
164.76
212.18
181.90
252.20 	
15.77
768.74
784.06
861 . 18
2,973.73
3,514.08
3,318.18
CBWH
1,155
1,430
1,430
306.05
319.82
319.82
163.34
166.74
77.12
62.83
595.68
598 . 56
1,750.68
2,028.56
2,064.67
CBWH
Terraces
C Conv. C Chisel CR Nn-t-i'l
1,155 4,480
1,430 5,480
1,358.50 5.480
310.64
326.51
160.22
163.62
104.35
90.05
576.81
649.29
134.87
148.47
204.36
183.96
619.74 977.24
624.71 1,042.92
1,774.74
2,054.71
2,019.32
5,457.24
6,522.92
6,578.43
4,480
5,480
5,480
576.81
649.29
134.87
148.47
204.36
183.96
972.22
1,037.90
5,452.22
6,517.90
6,573.41
2,345
2,870
2 597
370.55
409.37
151.16
157.96
212.18
181.90
768.74
784.08
3,113.74
3,654.08
3,458.18
          ttoteg! c • corn-beanj CB • corn-bean; CBWM - corn-bean-wheat-meadow.
          'Derivation shown in Table A-10.   See footnotes  Table A-10,  "Pesticide costs  from Table A-8E.

-------
                                      Table A-12E.   Stannary — Insecticide Scouting Alternative
Item
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CBHM
CB Conv. CB Chisel CB No-till Part. No-till
CBHM
Ko-till, Herb.
Terraces
C Conv. C Chisel
CB Mo-till
Ln
Gross Revenue, $
A uplands
B ridge
C lowlands
Costs
Tractor (excl.
fuel)
Implements
(excl. fuel)
Fuel
Seed
A uplands
B ridge
C lowlands
Fertilizer
A uplands
B ridge
C lowlands
Pesticides*
A uplands
B ridge
C lowlands
Labor
Terracing
Other**
A uplands
B ridge
C lowlands
52,500
65,000
65,000
4,604.91
10,643.65
1,426.99
2.380
2,620
2,860
7,540
8,487.50
8,487.50
4,808.50
4,328.50
5,291
2,149.42
0
5,177.24
6,242.92
6,298.53
Total Cost iNet of
Land Cost)
A uplands 38,790.71
B ridge 40,503.89
C lowlands 41,762
Net Return (Excl.
Land Cost)
A uplands
B ridge
C lowlands
Notes: C = corn;
13,769.29
24,496.11
23,238
52,500
65,000
65,000
4,537.42
10,365.66
1,336.54
2,380
2,620
2,860
7,540
8,487.50
8,487.50
4,808.50
4,328.50
5,291
2,019.30
0
5,172.22
6,237.91
6,293.41
38,159.64
39,933.05
41,190.83
14,340.36
25,067.17
23,809.17
CB • corn-bean; CBWM
49,875
65,000
52,000
4,281.19
8,913.07
1,120.86
2,500
2,740
2,980
7,947.50
9 , 007 . 50
9,007.50
9,031
8,383.50
9,676
1,708.98
0
5,167.66
6,444.83
5,473.36
40,670.26
42,599.43
43,160.97
9,204.74
22,400.57
8,839.03
46,312.50
59,125
59,125
4,272.26
11,134.31
1,073.97
2,540
2,660
2,780
4,658.75
5,106.25
5,106.25
3,470.06
2,817.56
4,349.06
1,691.76
0
2,897.43
3,435.73
3,507.63
31,738.54
32,191.59
33,915.24
14,573.96
26,933.41
25,209.76
46,312.50
59,125
59,125
4,301.95
10,828.87
1,113.78
2,540
2,660
2,780
4,658.75
5,106.25
5,106.25
3,470.06
2,817.56
4,349.06
1,671.68
0
2,899.64
3,437.94
3,509.84
31,484.73
31,938.03
33,661.43
14,827.77
27,186.97
25,463.57
44,437.50
57,875
50,712.50
4,056.64
9,376.28
898.10
2,667.50
2,787.50
2,907.50
4,843.75
5,351.25
5,351.25
4,992.56
4,280.06
5,934.06
1,361.36
0
2,973.73
3,514.08
3,318.18
31,169.92
31,625.27
33,203.37
13,267.58
26,249.73
17,509.13
43,031.25
51,781.25
49,906.25
4,734.71
14,493.38
1.508.40
2,882.50
2,942.50
3.OO2.50
4,OO0.63
4,180.63
4,180.63
1,814.56
1,478.31
2,247.89
2,215.42
0
1,750.68
2,028.56
2,064.67
33,400.28
33,581.91
34,447.62
9,630.97
18,199.34
15,458.63
43,031.25
51,781.25
49,012.50
4,672.41
13,728.36
1.429.91
2,912.50
2,972.50
3,032.50
4,060.63
4,268.13
4,abH.13
2,455.18
2,118.93
2,888.52
2,095.30
0
1,774.74
2,054.71
2,019.32
33,129.03
33,340.25
34,134.45
9,902.22
18,441.00
14,873.05
56,000
68 , 500
68,500
4,604.91
10,643.65
1,426.99
2,380
2,620
2,860
7,540
8,487.50
8,487.50
4,808.50
4,308.50
5,291
2,149.42
6,460
5,457.24
6,522.92
6,578.43
45,470.89
47,243.89
48,501.90
10,529.11
21,256.11
19,998.10
56,000
68 , 500
68,500
4,537.42
10,365.66
1,336.54
2,380
2,620
2,860
7,540
8,487.50
8. 487. 50
4,808.50
4,328.50
5,291
2,019.30
6,460
5,452.22
6,517.90
6,573.41
44,899.64
46,672.82
47,930.83
11,100.36
21,827.18
20,569.17
47,4J7.-J.'1
60,875
53,712.30
4,056.64
9,376.23
893.10
2,667. 50
2,787.50
2,907.50
4,843.75
5,351.25
5.351.25
4,992.56
4,280.06
5,934.06
1,361.36
6,460
3,113.74
3,654.08
3,458.18
37,769.87
38,225.27
39,803.37
9,667.63
22,649.73
13,909.13
= corn-bean-wheat-meadow.
                Pesticide costs from Table  A-8E, **Other  COStS from Table  A-10E.

-------
gross revenue in Table A-12E is the same as in. Table A-12; the scouting and




selected treatment with the recommended insecticide dosage has insured




that there is no yield loss under this alternative.  Net returns have




been increased slightly, approximately $1,000 for all options except the




continuous corn no-tillage options for which revenue increased by $2,350.






     Table A-13E shows the net revenue ranking of the farming practice




options under the scouting alternative.  When compared with Table  A-13,




it can be seen that the revenue changes caused by reducing insecticide




use through scouting are not significant enough to cause many changes




in ranking of the options.  When each soil type is considered separately




the only ranking change which occurs is the movement of the continuous




corn no-tillage option from ninth to seventh place on the ridge soils.




When all soils are considered together  the only change is that net




revenue increases slightly and the continuous corn no-tillage option on the




ridge soil moves up two places.  The revenue for the two continuous corn no-




tillage options on the uplands and lowlands has been significantly increased as




shown by the lower net revenue bound change from $6,000 in Table A-13  to




$8,000 in Table A-13E.  The relative net  return of  these  two  options  is




so low in the base case, however, that their ranking is not affected by




the revenue increase.  The three continuous corn no-tillage options are




most affected by the pesticide scouting alternative because in the base




case they require the most insecticide; for the other options, insecticide



costs are not high enough relative to other production inputs for finan-




cial returns to be significantly altered by their reduction.   Pesticide




costs account for 8 to 26 percent of total costs, depending on the




farming practice used and insecticide costs are 30 to 40 percent of





                                   156

-------
    pesticide costs.  More interesting results might be gained by applying




    the scouting option to the increased energy cost scenario where it might




    serve to reduce a very expensive input.
       Table A-13E.




      Uplands
Net Revenue Ranking—Insecticide Scouting Alternative




  Ridge           Lowlands           All Soils
high CB Chisel CB Chisel CB Chisel r
CB Conv. CB Conv. CB Conv. r
C Chisel CB No-till C Chisel r
C Conv. C Chisel C Conv. 1
CB No-till C Conv. C Chisel-Ter. 1
C Chisel-Ter. CB No-t.-Ter. C Conv.-Ter. r
C Conv.-Ter. C No-till CB No-till r
CBWM-Herb. C Chisel-Ter. CBWM-Part. 1
CB No-t.-Ter. C Conv.-Ter. CBWM-Herb. 1
CBWM-Part. CBWM-Herb. CB No-t.-Ter. r
low C No-till CBWM-Part C No-till r
r
r
r
1
1

r
r
1

1
1
u
u
u

1
u
u

u
u
u
u
u

u
1

CB Chisel
CB Conv. ^_
CB No-till
CB Chisel
CB Conv.
C Chisel
C Conv.
-«-
C Chisel
C Conv.
CB No-till-Ter.
C No-till ^
C Chisel Ter.
C Conv.-Ter.
C Conv.-Ter.
C Chisel-Ter.
C Conv.-Ter.

CBWM-Herb.
CBWM-Part.
CB No-till
•«-
CBWM-Part .
CBWM-Herb.
CB Chisel
CB Conv.
C Chisel
+•
CB No-till-Ter.
C Conv.
CB No-till
•«-
C Chisel-Ter.
C Conv.-Ter. ^_
CBWM-Herb.
C CB No-till-Ter.
CBWM-Part . ,
•<-
C No-till
C No-till
•«-
28,000

26,000




24,000



22,000




20,000



17 ,000





14 ,000



13 ,000

10,000



9,000


8,000
Notes;  C  =  corn; CB  =  corn-bean; CBWM  =  corn-bean-wheat-meadow.




        r  =  ridge; 1  =  lowlands; u  =  uplands.




                                        157

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                 Alternative F:  No Insecticide Treatment

      This alternative is the extreme end of the variation examined in

 Alternative E.   In this case no insecticide treatments are used for

 any of the options.   Table A-8F shows that pesticide costs have been

 reduced by the  elimination of insecticide costs;  only herbicide costs

 remain.   Total  pesticide costs have  been decreased by approximately

 $1,000 to $4,500 or  by 30 to 45 percent  depending on the  farming prac-

 tice (compare with Table A-8) .



      In Table A-10F these reduced pesticide costs are translated into

 correspondingly reduced interest costs.   Corn drying costs are  also

 reduced since yield  loss occurs as a result of insect damage.   Table

A-11F shows the change in yield due to this loss caused by lack of insecti-


 cide treatment.   Losses  differ according to soil  types  and crop rotations

 used and are detailed in a  footnote  to Table  A-11F.  Crop loss, of course,

 reduces  gross revenue.   Comparing Table A-11F to Table A-ll, it can be seen

 that gross  revenue is reduced  significantly for the continuous  corn

 options  ($2,000)  but  only slightly for the  other  options  ($5 to $50).



      Table  A-12F summarizes the effects of reduced gross revenue and


reduced pesticide costs.  Net  returns for all  options have been increased
                                                   \
slightly compared to the base case (Table A-12):  about $600 for the continu-

ous corn chisel and conventionally tilled options;  approximately $3,000

for the continuous corn no-tillage options; and about $1,000 for  all other

options.


     The net revenue ranking of all options under this alternative is

shown in Table A-13F.  As was  true for Alternative  E, there are 'relatively


                                  158

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                               Table A-8P.  Pesticide Costs — No Insecticide Treatment Alternative
Item
Tillage Practices
C Conv. C Chisel C No-till
Rotations
CBWM
CB Conv. CB Chisel CB No-till Part. No-till
CBWM
No-till, Herb.
Terraces
C Conv. C Chisel Cb :;o-til
Ln
Corn , Cost
Herbicide, $/acre
A uplands
B ridge
C lowlands
Acres
Total Cost, $
A uplands
B ridge
C lowlands
Soybeans, Cost
Total Cost, $*
A uplands
B ridge
C lowlands
Total Pesticide
Cost, $
A uplands
B ridge
C lowlands
Notes: C - corn;
*
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860

3,377.50
2,897.50
3,860
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860

3,377.50
2,897.50
3,860
CB - corn-bean; CBWM =
26.78
24.19
29.36
250
6,695
6,047.50
7,340

6,695
6,047.50
7,340
13.51
11.59
15.44
125
1,688.75 1
1,448.75 1
1,930 1
1,720 1
1,307.50 1
2,112.50 2
3,408.75 3
2,756.25 2
4,042.50 4
13.51
11.59
15.44
125
,688.75
,448.75
,930
,720
,307.50
,112.50
,408.75
,756.25
,042.50
24.50
22.26
26.78
125
3,062.50
2,782.50
3,347.50
1,868.75
1,436.25
2,280
4,931.25
4,218.75
5,627.50
13.51
11.59
15.44
62.5
844.38
724.38
965
934.38
718.13
1,140
1,778.76
1,442.51
2,105
23.76
21.84
25.69
62.5
1,485
1,365
1,650.63
934.38
718.13
1,140
2,419.33
2,083.13
2,745.64
13.51
11.59
15.44
250
3,377.50
2 , 897 . 50
3,860

3,377.50
2,307.50
3,860
13.51
11.59
15.44
250
3,377.50
2,897.50
3,860

3,377.50
2,897.50
3,860
24.50
22.26
26.78
125
3,062.50
2,782.50
3,347.50
1,868.75
1,436.25
2,280
4,931.25
4,218.75
5,627.50
corn-bean-wheat-meadow .
           *  See Table A-8 for derivation; see footnotes Table A-8.

-------
                                Table A-10P.  other Costs - No  Insecticide Treatment Alternative
cr>
o
Item
Corn Drying
Tillage Practices
C Conv.

Grain harvested, bu. *
A uplands 25.250
B ridge 31,500
C lowlands 31,500
Total Cost
A uplands
B ridge
C lowlands
4,040
5,040
5,040
Interest on Operating
Capital**
Fertilizer (8 so.)
A uplands 576.81
B ridge 649.29
C lowlands 649.29
A uplands
B ridge
C lowlands
Pesticide (6 mo.)*
A uplands
B ridge
C lowlands
Fuel (3 so.)
Labor (3 mo.)
Total Interest
A uplands
B ridge
C lowlands
Total Other Costs
A uplands
B ridge
C lowlands
134.87
148.47
162.07
143.54
123.14
164.05
30.32
30.88
916.42
982.10
1,081.02
4,956.42
6,022.10
6,077.61
C Chisel
25,250
31,500
31,500
4,040
5,040
5,040
576.81
649.29
649.29
134.87
148.47
162.07
143.54
123.14
164.05
28.40
27.78
911.40
977.08
1,032.59
4,951.40
6,017.08
6,072.59
C No-till
Rotations
CB Conv. CB
23,937.50 13,775.62
31,50O 17,056.87
25,000 17,034.37
3,830 2,204.10
5,020 2,729.10
4,000 2,725.50
607.98 356.39
689.07 390.63
689.07 390.63
141.67 143.93
155.27 150.73
168.87 157.53
284.54 144.87
257.02 117.14
311.95 171.81
23.82 22.82
20.37 21.81
1,078.38 689.82
1,145.55 703.13
1,214.08 764.60
4,908.38 2,893.92
6,185.55 3,432.23
5,214.08 3,490.10
Chisel CB
13,775.62
17,056.87
17,034.37
2,204.10
2,729.10
2,725.50
356.39
390.63
390.63
143.93
150.73
157.73
144.87
117.14
171.81
23.67
23.17
692. O3
705.34
766.81
2,896.13
3,434.44
3,492.31
CBWM
No-till Part. No-till
13,775.62
17,056.87
15,328.12
2,204.10
2,729.10
2,452.50
370.55
409.37
409.37
151.16
157.96
164.76
209.58
179.30
239.17
19.08
15.77
766.14
781.48
848,15
2,970.24
3,510.58
3.3O0.65
7,215.94
8,934.69
8,916.41
1,154.55
1,429.55
1,426.63
306 . 05
319.82
319.82
163.34
166.74
170.14
75.60
61.31
89.46
32. 05
17.12
594.16
597.04
628.59
1,748.71
2,026.59
2,055.22
CBWM
No-till, Herb.
Terraces
C Conv.
7,215.94 27,000
8,934.69 33,250
8,469.54 33,250
	 0.16
1,154.55
1,429.55
1,355.13
310.64
326.51
326.51
160.22
163.62
167.02
102.82
88.53
116.69
30.28
14.25
618.21
623.19
654.75
1,772.76
2,052.74
2,009.88
O.16
4,320
5,320
5,320
576.81
649.29
649.29
134.87
148.47
162 . 07
143 . 54
123.14
164.05
30.32
30.88
916.42
982.10
1,037.61
5,236.42
6,302.10
6,357.61
C Chisel
27,000
33,250
33,250
	 0.16
4,320
5,320
5,320
576.81
649.29
649.29
134.87
148.47
162.07
143.54
123.14
164.05
28.40
27.78
911.40
977.08
1,032.59
5,231.40
6,297.08
6,352.59
CB No-till
14,650.62
17,931.87
16,203.12
	 0.16
2,344.10
2,869.10
2,592.50
370.55
409.37
409.37
151 . 16
157.96
164.76
209.58
179.30
239.17
19.08
15.77
766.14
781.48
848.15
3,110.24
3,650.58
3,440.65
                    corn; CB - corn-bean; CBWM - corn-bean-wheat-Beadov.
             From Table  A-11F, **See  footnotes Table  A-10, ***From Table A-8F.

-------
                         Table A-11F.  Revenue — No Insecticide Treatment Alternative

I tea
	 Tillage Pra
C Conv. C Chisel
c'ices
C No-till
Rotations
CB Conv. CB Chisel CB No-till
CBWM
Part. No-till
CBWM
No-till, Herb.
Terraces
C Conv. C Chisel CB No-till
Corn
Expected yield.
bu/acre **
A uplands 105
B ridge 130
C lowlands 	 130
Loss*
A uplands 1,000
B ridge 1,000
Total output, bu.
A uplands 25,250
B ridge 31,500
Expected price/
Gross Revenue, $
A uplands 50,500
B ridge 63,000
Soybeans
Gross Revenue, $**
A uplands
B ridge
Wheat
Gross Revenue, $**
A uplands
B ridge
TOTAL GROSS
REVENUE, $
A uplands 50,500
B ridge 63,000
Notes: C - corn,- CB -


105 99.75
130 130
130 104
1,000 1,000
1,000 1,000
1,000 1,000
25,250 23,937.50
31,500 31,500
31,500 25,000

50,500 47,875
63,000 63,000







50,500 47,875
63,000 63,000
63,000 50,000


110.25 110.25
136.50 136.50
136.50 136.50
5.63 5.63
5.63 5.63
28.13 28.13
13,775.62 13,775.62
17,056.87 17,056.87
17,034.37 17,034.37

27,551.24 27,551.24
34,113.74 34,113.74
34,068.74 34,068.74

18,750 18,750
25,000 25,000
25,000 22,500





46,301.24 46,301.24
59,113.74 59,113.74
59,068.74 59,068.74


110.25
136.50
122.85
125
5.63
5.63
28.13
13,775.62
17,056.87
15,328.12
2
27,551.24
34,113.74
30,656.24

16,875
23,750
20,000





44,426.24
57,863.74
50,656.24


115.50
143
143
62 50
2.81
2.81
21.09
7,215.94
8,934.69
8,916.41
2
14,431.88
17,869.38
17,832.82

8,437.50
11,875
10 , OOO
7,031.25 	
13,215
15,000
15,000

43,025.63
51,775.63
49,864.07


115.50
143
143
62.50
2.81
2.81
21.09
7,215.94
8,934.69
8,469.54
2
14,431.88
17,869.38
16,939.08

3,437.50
11,875
10,000
7,031.25
13,215
15,000
15,000

43,025.63
51,775.63
48,970.32


112
137
137
250
1,000
1,000
1,000
27,000
33,250
33,250
2
54,000
66,500
66,500







54 , 000
66,500
66,500


112
137
137
250
1,000
1,000
1,000
27,000
33,250
33,250
2
54,000
66,500
66,500







54,000
66,500
66,500


117.25
143.50
129.85
125
5.63
5.63
28.13
14,650.62
17,931.87
16,203.12
2
14,644.99
17,926.24
32,406.24

18,125
25,000
21,250





47,426.24
60,863.74
53,656.24
corn-bean; CBWM - corn-bean-wheat-meadow.
, e to lacK o* ^«o«^»-i^i^» t-^-*»»*-nM>nt- Assume 10 bu/acre loss on 40% of

on 5% and 7.5%, respect]
ively, of corn acreage
^..Sn^H» Insurance, Potential Management Tool for Corn Insects,"
acreage for continuous corn.
for the lowlands. Assume '
in Bulletin
For CB
1.5 bu/acre loss on 1%
and CBWM,
of acreage f

T*,CB
of the Entomological Society of America, Vol. 23, No. 3,
pp. 181-184, September 1977.




• •  For derivation see Table A-ll;  see footnotes,  Table A-ll.

-------
                       Table A-12P.  Suamary — No Insecticide Treatment Alternative
Item
Tillage Practices
C Conv. C Chisel

c ::=-tiii
. 	 Rotations
C3 Conv. CB Chisel CB No-till Pa

CBWM
rt. No-till

CBWM
No-till, Herb

	

i 	 : 	 :: — _



A uplands
B ridge
C uplands
Costs
Tractor (excl.
fuel)
(excl. fuel)
Fuel
Seed
A uplands
B ridge
C lowlands
A uplands
B ridge
C lowlands
A uplands
B ridge
C lowlands
Labor
Other***
A uplands
B ridge
C lowlands
Total Cost (Net
Land Cost)
A uplands
B ridge
C lowlands
Land Costs)
A uplands
a ridge
C lowlands
Notes: C = corn
50,500
63,000
63,OOO
4,604.91
10,643.65
1,426.99
2,380
2,620
2,860
7,540
8,487.50
8,487.50
3,377.50
2,897.50
3,860
2,149.42
0
4,956.42
6,022.10
6,077.61
of
37,078.89
38,852.07
40,110.08
13,421.11
24,147.93
22,889.92
; CB - corn-*
50,500
63,000
63,000
4,537.42
10,365.66
1,336.54
2,380
2,620
2,860
7,540
8,487.50
8,487.50
3,377.50
2,897.50
3,860
2,019.30
0
i —
4,951.40
6,017.08
6,072.59
36,507.82
38,281
39,539.01
13,992.18
24,719
23,460.99
bean ; CBWM
47,875
63,000
50.0OO
4,281.19
8,913.07
1,120.36
2,500
2,740
2,980
7,947.50
9,007.50
9,007.50
6,695
6,047.50
7,340
1,708.98
0
4,908.38
6,185.55
5,214.08
38,074.98
40,004.15
40,565.69
9,800. O2
22,995.85
9,434.31
46,301.24
59,113.74
59,068.74
4,272.26
11,134.31
1,073 97
2,540
2,660
2,780
4,658.75
5,106.25
5,106.25
3,408.75
2,756.25
4,042.50
1,691.76
0
2,893.92
3,432.23
3,490.10
31,673.72
32,126.78
33,591.15
14,627.52
26,986.96
25,477.59
46,301.24
59,113.74
59,068.74
4,301.95
10,828.87
1 113 78
2,540
2,660
2,780
4,658.75
5,106.25
5,106.25
3,408.75
2,756.25
4,042.50
1,671.68
0
2,896.13
3,434.44
3,492.31
31,419.91
31.873.22
33,337.34
14,881.33
27,240.52
25,731.40
44,426.24
57,863.74
50,656.24
4,056.64
9,376.28
2,667.50
2,787.50
2 , 907 . 50
4,843.75
5,351.25
5,351.25
4,931.25
4,218.75
5,627.50
1,361.36
0
2,970.24
3,510.58
3,300.65
31,105.12
31,560.46
32,906.28
13,321.12
26,303.28
17,749.96
43,025.63
51,775.63
49,864.07
4,734.71
14,493.38
1.508.40
2,882.50
2,942.50
4,000.63
4,180.63
4,180.63
1,778.76
1,442.51
2,105
2,215.42
0
1,748.71
2,026.59
2,055.22
33, 362. SI
33,544.14
34,295.28
9,663.12
18,231.49
15.568.79
= corn-bean-wheat-meadow.
43,025.63
51,775.63
48,970.32
4,672 41
13,728.36
1.429.40
2,912.50
2,972.50
4,060.63
4,268.13
4,268.13
2,419.33
2,083.13
2,745.63
2,095.30
0
1,772.76
2,052.74
2 , 009 . 88
33,019.25
33,302.48
33.932.12
9,934.38
18,473.15
14,988.20

54,000
66,500
66 500
4 604 91
10,643.65
1,426.99
2,380
2,620
7,540
8,487.50
8 487 50
3,377.50
2,897.50
3,860
6,460
5,236.42
6,302.10
6,357 61
43,818.89
45,592.07
46,850.08
10,181.11
20,907.93
19,649.92

54 , 000
66 , 500

10,365.66
1,336.54
2,380
2,620
7,540
8,187.50
3,377.50
2,897.50
3,860
6,460
5,231.40
6,297.08
43,247.82
15,021
46,279.01
10,752.18
21,479
20 020 99

47,426.24
60,853.74

9,376.2$
893. 1C
2,667.50
2,787.50
4,843.75
5,351.25
4,931.25
4,218.75
5,627.50
6,460
3,110.24
3,650.50
3 440 65
37,705.12
3B,160.4G
9,721.12
22,693.28

*From Table A-11F, **From Table A-8F, ***From Table A-10F.

-------
                                 Table A-13F




              Net  Revenue  Ranking —  No  Insecticide  Treatment Alternative
      Uplands
Lowlands
All Soils
high CB Chisel CB Chisel CH Chisel r
CB Conv. CB Conv. CB Conv. r
C Chisel CB No-till C Chisel r

C Conv. C Chisel C Conv. 1
CB No-till C Conv. C Chisel-Ter. 1
C Chisel-Ter. C No-till C Conv.-Ter. r
C Conv.-Ter. CB No-t.-Ter. CB No-till r
CBWM-Herb. C Chisel-Ter. CBWM-Part. 1
C No- till C Conv.-Ter. CBWM-Herb. r
CB No-t.-Ter. CBWM-Herb. CB No-t.-Ter. 1
low CBWM-Part. CBWM-Part. C No-till r
r

r
1
1

r
r
1
1
1
u
u
1
u
u
u
u
u
u
u
CB
u
1

-<-
CB Chisel
CB Conv.
CB No-till
-«-
CB Chisel
CB Conv.
C Chisel
C Conv. ^
C Chisel
C No-till
C Conv.
CB No-till-Ter.
C Chisel-Ter.
-«-
C Conv.-Ter.
C Chisel-Ter.
C Conv . -Ter .

CBWM-Herb.
CBWM-Part.
CB No-till ^
CBWM-Part
CBWM-Herb .
CB Chisel
CB Conv.
CB No-till-Ter.^
C Chisel
C Conv.
CB No-till ^_
C Chisel-Ter.
C Conv.-Ter. ^
CBWM-Herb .
C No-till
No-till-Ter.
CBWM-Part.
C No-till ,
•<-
28,000


26 , 000



24,000





21,000



19,000


17,000




14,000


13,000

10,000





9,000
Notes:   C  =  corn; CB  =  corn-bean;  CBWM  =  corn-bean-wheat-meadow.




         r  =  ridge; 1  =  lowlands; u  =  uplands.
                                       163

-------
few shifts in financial return position as a result of eliminating insec-

ticide treatment altogether.  The continuous corn no-tillage options on

the ridge and uplands moved up in the ranking because they bear the heavi-

est pesticide costs in the base case and this alternative relieved this

burden somewhat.  Net revenue improves for all options and particularly

for the continuous corn no-tillage options, one of which remains at the

bottom of the ranking, however.  Other shifts in position that occur

when all soils are ranked together are a result of slight differences

in gain or loss from the decreased revenue and decreased pesticide costs

and are not especially significant.


     The same conclusions can be drawn from this alternative as from

Alternative E, namely, that insecticide costs are not significant relative

to other productions costs and therefore even total elimination of these

costs (which account for at most 10 percent of total costs) will not

affect the farmer's choice of farming practice.  This is true even though

there is a reduction in yield caused by the lack of pesticide use; the

decreased pesticide costs more than make up for the lost revenue.  As

with Alternative E, it might be worthwhile to combine the no insecticide

treatment alternative with other alternatives that have been considered

such as the increased energy scenario.


REFERENCES, APPENDIX A

Allen County Soil and Water Conservation District.   "Cost  Sharing Formulae
    For Land Use Practices in Black Creek," effective January  1976, mimeo.*

Allen County Soil and Water Conservation District.   "Land  Use  Practices in
    Black Creek," mimeo.*

Beuerlein, J.E. and Bone, S.W.  "Selecting a Tillage System."   Ohio State
    University, Cooperative Extension  Service.   (Undated).


                                   164

-------
 Bone, S.W.  et al.   Reduced Tillage Systems for Conservation and Profitability.
     Ohio State Department of Agricultural Economics,  April 1976.

 Brink, L.;  McCarl,  B.A.  and Doster,  D.H.   Methods and Procedures in the
     Purdue  Crop Budget (Model B-9);  An Administrator's Guide.   Station Bulletin
     No.  121.   Purdue University,  Department of Agricultural Economics, March 1976.

 Carlisle, G.W. and  Griffith, D.R.   "Conservation Tillage Trials in Progress  in
     the  Black Creek Watershed."   Proceedings,  Best Management  Practices for
     NPS  Pollution Control Seminar,  Chicago,  Nov.  16-17 1976.

 Data Resources Inc.   "Data Resources Outlook for the  United States Energy
     Sector: Control Case."  Energy  Review.   Lexington, Mass.   Summer 1977.

 Doster,  D.H.   Indiana Custom Rates  for Power-Operated Farm Machines—1976.
     EC-130  (Rev).   Purdue University,  Cooperative Extension Service,  West
     Lafayette, Ind.

 Doster,  D.H.   "Purdue B-94 Linear Program Crop Budget—Explanation of Base
     Case Farm."  Summary.   Purdue University,  Department of Agricultural
     Economics (Undated).

 Doster,  D.H.  and Macy, T.   "The Time Resource  in the  Cornbelt  Farm Equipment
     Selection; Twenty-Four Years of  Weather  Data."  Purdue University,
     Department of Agricultural Economics,  July 1977.

 Edwards, C.R.  and Matthew,  D.L.   "Soil Insects Affecting Corn."   Pub  E-49.
     Purdue University, Cooperative Extension Service,  West Lafayette,  Ind.,
     January 1977.

 Goettl,  D.L.   "Total  and  Unit Costs  for Tile Outlet Terraces in Black  Creek."
     Memo to Dan McCain, USDA.  February 8, 1977.

 Gordon,  J.R.  and Quinn, T.R.  Profit of Employment and Income  in  Indiana.  EC-421,
     Purdue University, Cooperative Extension Service;  West Lafayette,  Ind.,  1973.

 Griffith, D.R.; Mannering,  J.V. and  Moldenhauer, w.c.  Conservation Tillage
     in the Eastern Corn Belt.  Journal of Soil and Water Conservation,
     Vol. 32,  No. 1, January-February 1977.

 Griffith, D.R. and Manning, J.V.  "Where is No Plow Tillage Adopted in
     Indiana?"  AY 185 Tillage, Agronomy Guide, Purdue University, Cooperative
     Extension  Service.

 Heath, M.E.  and Smith, L.H.   "Forage Mixtures for Indiana  Soils."  Agronomy
    Guide.   AY-18.   Purdue University, Cooperative Extension Service, West
    Lafayette.  (Undated).

Hunt, D.R.  and Rickey, C.B.   "Determining Usage Costs  for  Farm Tractors and
    Field Machines."  Agricultural Engineering.  AE-81.  Purdue University,
    Cooperative Extension Service, West Lafayette, 1971.
                                    165

-------
Iowa State University, Cooperative Extension Service.  Background Information
    for use with Corp-Opt System.  FM 1628, 8th revision, Ames, Iowa,
    December 1976.

Iowa State University, Cooperative Extension Service.  Crop-Opt System Input
    Farms.  FM 1627, 8th revision, Ames, Iowa, December 1976.

Iowa State University, Cooperative Extension Service.  Estimated 1977 Iowa
    Farm Custom Rates.  FM 1698, Ames, Iowa, Revised January 1977.

Iowa State University, Cooperative Extension Service.  Estimating Farm
    Machinery Costs.  PM-710, Ames, Iowa, November 1976.

Iowa State University.  "Worksheet for Estimating Farm Machinery Costs."
    PM-710a, Ames, Iowa, November 1976.

James, Sydney C., ed.  Midwest Farm Planning Manual.  Ames, Iowa:  Iowa
    State University Press, revised 1975.

Johnson, K.T. and White, W.C.  Energy Requirements for the Production of
    Phosphate Fertilizers.  Draft Abstract.  (Undated).

Pimentel, D.  Energy Inputs for the Production, Formulation, Packaging and
    Transport of Various Pesticides.  Draft Paper.  New York State College
    of Agriculture and Life Sciences; Ithaca, N.y.  November 21, 1977.

Pimentel, D. et al.  Alternatives for Reducing Insecticides on Cotton and Corn;
    Economic and Environmental Impact.  U.S. Environmental Protection Agency,
    Office of Research and Development.  February 1977.

Purdue University, Cooperative Extension Service.  Department of Agricultural
    Economics.  Community Data; Employment and Wages in Indiana 1963-1973.
    EC-310, West Lafayette, Ind., September 1975.

Purdue University, Cooperative Extension Service.  Farm Planning and Financial
    Management.  ID-68, Revised, West Lafayette, Ind., 1975.

Purdue University, Cooperative Extension Service.  Hired Farm Labor.  EC-459
    April 1977.

Purdue University, Cooperative Extension Service.  Indiana Custom
    Rates for Power Operated Farm Machines 1976.  EC-130, Revised.

Purdue University, Deptartment of Agricultural Economics.  Purdue Crop Budget,
    Model B-94.  July 15,  1977.

Richey, C.B. and Hunt, D.R.  "Determining Usage Costs for Farm Tractors and
    Field Machines."  Purdue University, Cooperative Extension Service, AE-81.

Sission, D.L. and Austin,  R.H.  Good Tile Drainage.  AE-66, Purdue University,
    Cooperative Extension Service, West Lafayette, Ind., 1966.
                                    166

-------
Triplett, G.B. Jr. and Van Doren, D.M. Jr.  "Agriculture Without Tillage."
    Scientific American, 1977.

Turpin, F.T.  "Insect Insurance: Potential Management Tool for Corn Insects."
    Bulletin of Entomological Society of America, Vol. 23, No. 3, September 1977,

Turpin, F.T.; Dumenil, L.C. and Peters, D.C.  "Edaphic and Agronomic
    Characters that Affect Potential for Rootworm Damage to Corn in Iowa."
    Journal of Economic Entomology, Vol. 65, No. 6, December 1972.

Turpin, F.T. and Maxwell, J.D.  "Decision-Making Related to use of Soil
    Insecticides by Indiana Corn Farmers."  Journal of Economic Entomology,
    Vol. 69, No. 3, June 1976.

Turpin, F.T. and Thieme, J.M.  "Impact of Soil Insecticide Usage on Corn
    Production in Indiana:  1972-1974".  Journal of Economic Entomology, 1977.

U.S. Department of Agriculture.  Crop Reporting Service.

    Agricultural Prices, Annual Summary 1976.  Pr 1-3(77).
    Agricultural Prices Released February 28, 1977.  Pr 1(2-77).
    Agricultural Prices Released April 29, 1977.  Pr 1(7-77).
    Agricultural Prices Released July 29, 1977.  Pr 1(7-77).
    Agricultural Prices Released October 31, 1977.  Pr 1(10-77).

U.S. Department of Agriculture.  Economic Research Service, Farm Costs and
    Returns, Agriculture Information Bulletin, No. 230, Washington, D.C.,
    September 1968.

U.S. Department of Agriculture.  Environmental Protection Agency.  Office of
    Research and Development, Control of Water Pollution from Cropland,
    Vol. 1: A Manual  for Guideline Development, EPA 600/2-75-026a, November
    1975.  Vol. II: An Overview, EPA 600/2-75-0266, June 1976.

U.S. Department of Agriculture.  Indiana Soil Conservation Service.  Technical
    Guide,  Section V, Installation and Amortized Costs for Major Conservation
    Practices.  Technical Guide, Section V, Indiana, April 1975.

U.S. Department of Agriculture.  Soil Conservation Service,  installation and
    Amortized Costs for Major Conservation Practices.   Technical Guide,
    Section V, Indiana, April 1975.

U.S. Department of Agriculture.  Soil Conservation Service.  Total and
    Unit Costs for Tile Outlet Terraces in Black Creek.  Memo from Dan McCain
    to Deone L. Goettl, February 8, 1977.

U.S. Department of Agriculture.  Soil Conservation Service.  "No Flow
    Tillage—A Conservation Tool for Indiana Farmers."  AY 192.  Purdue
    University, Cooperative Extension Service, West Lafayette, Ind.
                                    167

-------
U.S. Department of Agriculture.  Soil Conservation Service.  Allen County
    Soil Conservation and Water District.  Soil Conserving Tillage Systems.
    (Undated).

U.S. Department of Agriculture.  Statistical Reporting Service.  Annual Crop
    and Livestock Summary, 1976; Indiana Crop and Livestock Statistics.
    A-77-1, Purdue University, Agricultural Experiment Station, West
    Lafayette, Ind., August 1977.

White, W.C.  Energy Problems and Challenges in Fertilizer Production.  Paper,
    The Fertilizer Institute, Washington, D.C., December 4, 1974.

White, W.C.  Fertilizer—Food-Energy Relationships.   Paper, The Fertilizer
    Institute, Washington, D.C., August 28, 1974.

Wilson, C.D.  Environmental Impact of Land Use on Water Quality.  Operations
    Manual.  Black Creek Study, Allen County Indiana.  EPA-905-74-002.  U.S.
    Environmental Protection Agency, Region V, Allen County Soil and Water
    Conservation District, March 1974.

* Informal mimeos entitled by Meta Systems, Inc.
                                    168

-------
                              Appendix B




              Methods for Predicting Watershed Loadings




Introduction






     The methods described below have been developed to assess the




impacts of agricultural practices on nonpoint pollutant loadings.  The




models are of an empirical nature and are concerned with long-term aver-




age emissions, in the spirit of the Universal Soil Loss Equation  (Wisch-




meier and Smith, 1972).  Average export rates of the following substances




are evaluated in surface runoff and in subsurface drainage:






     1)  Sediment (sand, silt, and clay fractions);




     2)  Phosphorus  (extractable particulate and soluble);




     3)  Soluble nitrogen; and




     4)  Dissolved color.






The computed concentrations of these components are assumed to be repre-




sentative of average water quality conditions in rivers draining the




agricultural watersheds.  The methodology is appropriate for linking




with downstream models for the purpose of evaluating quality impacts in




impounded waters, as discussed in a subsequent section  (see Methods for




Predicting Impoundment Water Quality).






     Using the generalized pathways depicted in Figure  B-l, emissions  are




computed as functions of the following characteristics:





                                   169

-------
WATERSHED
CHARACTERISTICS
  Field Characteristics
  Soil Characteristics
  Climate
  Morphorr.'jtry
  AQf'iculturai
    Practices
  Crop Yields
TRANSPORT RATES
   Sediment
   Runoff
   Percolation
TRANSPORT  MEDIA
COMPOSITION
   Sediment
   Runoff
   Percolation
                               Nitrogen Budget
AVERAGE  RIVER
WATER QUALITY
AND COMPONENT
LOADINGS
  Sediment
  Phosphorus
  Nitrogen
  Color
                 Figure B-l.  Pathways in Predicting Watershed Loadings

-------
     1)  Surface Soil Properties

         a) Erodibility  (K factor in USLE, Wischmeier and Smith, 1972);

         b) Texture  (sand, silt, and clay content);

         c) Hydrologic Soil Group (SCS/USDA, 1971);

         d) Extractable phosphorus content  (in each texture class);

         e) Phosphorus distribution coefficient  (g extractable P/kg
            Soil)/(g dissolved P/m3 soil solution); and

         f) Organic matter content (in each texture class).

     2)  Watershed/Field Properties:

         a) Slope;

         b) Slope length;

         c) Surface area;

         d) Total flow (runoff and drainage); and

         e) Rainfall erosivity  (R factor in USLE);

     3)  Agricultural Practices:

         a) Cropping factor (C in USLE)

         b) Practice factor (P in USLE)

         c) Nitorgen and Phosphorus fertilization rates;

         d) Tillage depth; and

         e) Crop residue management.


The methodology is based upon the Universal Soil Loss Equation  (USLE),

which has been developed by the USDA for use in the soil conservation

area.  This equation and its tabulated parameter estimates are based

upon a large data-collection and analytical effort.  A number of addi-

tions have been made in this study in order to make the USLE a more use-

ful tool for evaluating water quality impacts.  The formulations and


                                    171

-------
calibrations of the additional functions are based upon substantially




less data and analysis than  the USLE and could therefore be described




as less objective.  Analysis of further experimental and monitoring data




could lead to a more objective basis for some of the assumed functional




forms and parameter estimates.  However, for this study relatively sub-




jective assessments are relied upon,— substantiated when possible with




data and the opinions of experts.  A sensitivity analysis will help to




determine which assumptions are most important in evaluating both the




absolute and the relative impacts of agricultural practices on watershed




emissions and on downstream water quality.






     The methodology is applicable to a single field or plot of uniform




characteristics.  In this preliminary assessment of agricultural prac-




tices, a hypothetical watershed is assumed to be comprised of a number




of fields of equal characteristics.  This provides a rough measure of




the unit emissions and water quality impacts of a given field/soil type/




agricultural practice combination.  The methodology could be applied to




a heterogeneous watershed consisting of a number of areas, each with its




own set of field/soil type/practice specifications.  The effects of




heterogenous watershed characteristics on practice evaluations and con-




clusions are considered higher level questions, which are best addressed




subsequent to an analysis of homogenous watersheds.






     In order to be compatible with the economic analysis carried out in




this study the models are calibrated to three different field/soil types




which are characteristic of the Black Creek Watershed, Indiana.  A




research and demonstration program sponsored by the EPA  (Christenson and





                                     172

-------
Wilson,  1976,  Lake and Morrison,  1975,  has provided some data for cali-



brating the models to these three field and soil types.  In the discus-



sion below, general (i.e., process-related) parameter estimates are pre-



sented immediately after the corresponding functions.  Soil- and practice-



specific parameters are presented and discussed in a separate section.  In



view of the preliminary nature of many of the functional forms and parame-



ter estimates, a final sensitivity analysis is essential to understanding



and assessing the feasibility of applying the methodology in a planning



context.







Sediment





     Estimation of gross sheet and rill erosion rates are obtained



through use of the Universal Soil Loss Equation  (Wischmeier and Smith, 1972)





     S =  .224 RKL  PC                                                 (1)





where,



     S =  gross erosion rate  (kg/m  -yr)



     R =  rainfall erosivity factor



     K =  soil erodibility factor  (tons/acre-year)



     L  = length/slope factor
      S


     P =  practice factor



     .224 = dimensional factor ((kg/m2)/(tons/acre))





The C factor  is computed  considering  the  seasonal  variations  in  soil



cover and rainfall erosivity, as prescribed by Wischmeier and Smith.



Detailed  discussions of the bases, assumptions and parameter  estimates




                                    173

-------
of this model are available elsewhere (Wischmeier and Smith, 1972,



Wischmeier, 1976, EPA and USDA, 1975, and MRI, 1976).




     The length/slope factor is computed using the following function



(Wischmeier and Smith, 1972):





     L  = /T (.0076 + .0053g + .0076g2)                              (2)
      s




where,





     L = length of slope (feet)



     g = slope gradient (percent)





     Eroded sediment is usually enriched in fine particles, relative to



the surface soil of its origin.  This enrichment is apparent in edge-of-



field sediment measurements (Soltenberg and White, 1953; Kilner, 1960),



in river sediments measurements  (Rausch and Heinemann, 1975; Jones et al,



1977) and in lake bottom sediment measurements (Stall, 1972).  Since



finer fractions of soil have higher surface areas per unit mass, they



generally have higher adsorption capacities and higher nutrient and



organic matter contents, expressed as grams per gram of solid (Buckman



and Brady, 1960).





     Enrichment of fine particles in sediment is considered here in



order to permit explicit calculation of the nutrient and organic matter



contents of eroded sediment based upon the measured nutrient and



organic matter contents of various soil size fractions.  This is an



alternative to the use of gross "enrichment ratios" (MRI, 1976).



By explicitly considering the clay, silt, and sand fractions in soil




                                   174

-------
and eroded sediment, differences in the behavior of these fractions in




rivers and in impoundments can be modeled.  This also forms a basis for




future development of models for other constitutents, such as biocides




or biocide residues, which may also show preferential adsorption to



fine particles.







     The enrichment phenomenon has been shown to increase with decreas-




ing gross erosion and runoff rates.  For instance, Stoltenberg and White




(1953) found that the clay content of eroded material from a soil con-




taining 16 percent clay increased from 25 percent to 60 percent as run-




off rates decreased from 2.84 to .01 inches/hour.  Raush and Heinemann




(1976) found that the clay fraction in river sediment from a watershed




in Missouri increased from 30 percent to 80 percent as peak storm flows




decreased from 10 to .3m3/sec.  An empirical function for computing




phosphorus enrichment ratios developed by Massey et al (1953) and pre-




sented by MRI (1976) is qualitatively consistent with this behavior, in




that it predicts an increase in the phosphorus enrichment ratio, given




a decrease in either the total sediment concentration or the total




erosion rate.






     In order to account for enrichment, the texture of eroded sediment




is computed as a function of soil texture and S, the gross erosion rate,




using the following assumed relationships:







     XCL = XCL + (XSL - XCL}  <  Kl  )                                <3>
      CL    CL     CL    CL
                                   175

-------
         = X    (TT--JT)                                               (4)
      SA    SA  S + K2
                CL - XSA
               xs

     YM  _ n   _§

     XCL - I - 1E
where,
     X   , X   , X    = clay, silt, and  sand  fractions of  eroded  sediment
      CL   SX   SA
      S    S    S
     X   , X   , X    = clay, silt and sand  fractions of  surface  soil
      wJ-i   S X   S A
     Ki, K2, KB  = empirical parameters




     X   = maximum clay content of eroded sediment





According to these equations, sediment texture approaches that of  surface



soil as S approaches infinity, while the clay, silt, and sand fractions


          M       M
approach X , 1 - X , and 0 as S approaches zero.  The  following tentative

          CL      CL

parameter values are assumed:





     Kj = .50 kg/m2 - year



     K2 = 20.0 kg/m2 - year




     K3 = 2.0
                                   176

-------
The behavior of sediment texture as a function of S for these parameter



values and for a typical soil texture is depicted in Figure B-2.   While



explicit, quantitative justification for the assumed parameter values



cannot be given, sediment texture computed according to this scheme



agrees qualitatively with the data discussed above.  Direct calibration



and testing should be done,  when the appropriate data are available.




     Estimates of gross erosion for each texture class are converted to


watershed emission rates by application of a sediment delivery ratio,



which is computed as a function of downstream watershed area and texture



class:





     SCL - SXCL DdCL                                                  (7)
     Ssi - sxsi Ddsi
     SSA
where,
      °, S° , S^_ = delivered clay, silt and sand  (kg/m2 - year)
      CL   SX   SA
     D  = reference delivery ratio



     d  , d  , d   = delivery ratio multiplier for clay, silt and
      C»L   S X   S A


                     sand fractions.
Total watershed area has been often used as an independent variable  for



predicting mean sediment delivery ratios  (EPA/USDA,  1976;  Vanoni,  1975)




                                    177

-------
H1
-j
GO
         1.0
          .8
       o>

       2
       X
c  .4
0)
£
T3

-------
Data from a table in EPA/USDA (1976),  have been fit to the following empiri-


cal function:




     5  = K4 A;KS                                                     do




where



     Kt»  =  .34



     K5  =   .20


                                     i)
     A   =   total watershed area  (km )



     5   =   mean delivery ratio



While other factors have been employed as delivery ratio predictors,  the


above functional form has been most widely used  (Vanoni, 1975).  In a


heterogeneous watershed, however, direct application of equation  (10)


to the area  mean gross erosion rate could lead to errors, because it


does not take into account the fact that delivery ratios are likely to


be higher in the lower contours of a watershed than in the upper contours,


due to shorter transport distances.  This can be demonstrated  quantita-


tively.  By differentiating the product of the total watershed area and


the average delivery ratio  (computed according to equation  (10)), it  can


be shown that equation  (10) implies the following:
                                    179

-------
where,
     D   = localized delivery ratio for a region at the uppermost con-


      AD

           tour of a watershed


                                       A

     A  = watershed area downstream (km )
D   is a localized delivery ratio, whereas D, in equation  (10), repre-

  D

sents the average value over an entire watershed.  Equation  (11) pre-



dicts lower effective delivery ratios in higher areas within a given



watershed.  For application in heterogeneous watersheds, the D value in



equations (7) to  (9) should be computed for each sub-area using equation



(11) and the downstream watershed area, as opposed to the total water-



shed area.  In homogeneous watersheds, results are independent of



whether equation  (10) or equation (11) is used.





     A graph in MRI  (1976) indicates that delivery ratios for clay, silt,



and sand are approximately in the ratios 5:3:1.  If these ratios are



normalized to a d .  value of 1, the following delivery ratio multipliers
                 S J.


are calculated:
         = 1.67
     dsi = i.oo
     dSA ' °'
These multipliers are assumed to be independent of location in a given



watershed.
                                    180

-------
     The total sediment load transported to a downstream impoundment is



computed as the sum over the texture classes multiplied by the ratio of



watershed area to impoundment surface area:

where
      D                                  2
                                         2
     S  = impoundment sediment load (kg/m  surface area-year)
     A  = impoundment surface area  (km2)
The computed sediment delivery of each texture class is used to estimate



sedimentation rate, phosphorus trapping rate, and suspended solids con-



centration in the impoundment, according to the methodology discussed



separately (see Appendix C).






Runoff and Percolation




     Predictions of the emissions of soluble phosphorus and color are



dependent upon estimates of average surface runoff rates.  The total flow



rate from a watershed or field is assumed to consist of two components,



the sum of which is independent of the agricultural practice:




     q - q  + q                                                       (13)
where
     q = total flow rate  (m/year)



     qR = surface runoff rate  (m/year)



     qD = subsurface drainage  (m/year)

                                   181

-------
 This  essentially  assumes  that  average  evapotransporation rates  are  not




 significantly influenced  by  the mode of  farm operation.   The  runoff




 component,  q ,  is evaluated  as:
             K
      qR  -  qd-fR)                                                    (14)
where
     q = baseline  runoff rate for  straight-row, continuous  corn on  soil
       R,



          of the appropriate hydrologic group  (m/year)




     f = runoff reduction factor appropriate  for agricultural practice
       K



          and  soil  type.
This method is based upon the results of simulations performed by Wool-




hiser  (1975, 1977), using a modification of the SCS Curve Number runoff




model  (SCS, 1971).  These simulations have provided regional estimates




of average annual  runoff rates for soils in various Hydrologic Groups




(SCS,  1971) and for two basic agricultural practices:  straight row,




continuous corn and continuous meadow, which represent the approximate




upper  and lower limits of q , respectively, as influenced by agricultural
                           K



practice.  The former are used here as reference values and equated to




q  for the appropriate soil group and region.  Some of Woolhiser's simu-




lation results are summarized in Table B-l. Regional variations in q  are




shown in  FiguresB-3 through B-6 for  soils in various Hydrologic Groups.






     Values of f  are sensitive both to soil type and to agricultural
                I\



practice, since some practices are only effective on certain soil types.



Estimation of f  values is based upon Woolhiser's Table 14 and Figure 32
               I\
                                    182

-------
                                 Table  B-l.   Results of Direct Runoff  Simulations  (EPA/USDA,  1975)


Location

Wichita, KS
Columbia, MO
Columbus, OH
Des Moines, IA
Grand Isl., NB
Sioux Fall, SD
Cairo, 1L
Indianapolis, IN
Springfield, IL
Houston, TX
Raleigh, NC
Charleston, WV
Birmingham, AL
Columbia, SC
Dallas, TX
Little Rock, AR
Buffalo, NY
Boston, MA
Scranton, PA
Pittsburgh, PA
Seattle, WA

Hydro logic
soil group

B
D
C
B
B
B
B
C
B
D
B
C
B
B
D
D
B
A
C
C
B
Estimated
1
direct runoff
(inches)

2.2
5.3
3.6
1.6
1.5
1.2
4.7
5.2
2.6
11.3
2.4
4.0
7.2
4.4
8.3
13.4
1.5
2.2
2.6
3.2
2.9
% reduction in annual runoff

Contouring,
R9
11
20
12
18
16
8
1
11
12
17
16
14
11
17
15
12
13
6
16
10
20
Contoured and
terraced, R 9, R 1 2
22
37
21
27
23
16
9
21
22
36
32
25
21
31
32
24
23
15
30
19
35
Meadow
R16
81
75
75
89
88
94
78
75
89
52
88
75
72
83
55
58
89
94
82
83
85
Estimated
mean growing
season direct
runoff (inches)

1.7
2.9
1.0
0.9
0.9
0.7
1.3
1.7
1.4
5.9
1.1
1.2
1.8
2.3
5.1
5.5
0.7
0.6
0.8
0.9
0.1
% reduction in growing season runoff

Contouring,
R9
15
31
10
24
12
13
11
23
12
17
19
25
14
21
14
11
33
11
21
22
33
Contoured and
terraced, R 9, R 1 2
29
53
24
38
26
28
24
42
24
36
39
36
29
39
29
24
54
26
32
41
55
Meadow
R16
80
68
73
85
90
95
80
74
83
49
88
62
74
82
53
57
100
85
78
85
89
00
OJ
           1 More than 4,000 soils in the United States and Puerto Rico have been assigned by the Soil Conservation Service to Hydrologic soil groups A through D on the basis of their
        runoff potential. Hydrologic group A has low runoff potential; group D has a high runoff potential; and B and C are intermediate. For a more detailed discussion, see Volume II,
        Appendix A.

-------
00
             Figure  B-3.
Mean Annual Potential Direct Runoff in Inches. Straigt-row Corn in Good
Hydrologic Condition — Hydrologic Soil Group A.  (woolhiser   1976)

-------
00
                                                                                             '/.o
                    Figure B-4.
Mean Annual Potential Direct Runoff in Inches.  Straight-row Corn
in Good Hydrologic Condition — Hydrologic Soil Group B.
(Woolhiser, 1976)

-------
00
                                                                                                 0
                    Figure B-5.
Mean Annual Potential Direct Runoff in Inches.  Straight-row Corn
in Good Hydrologic Condition — Hydrologic Soil Group C
(Woolhiser (1976)

-------
00
                 Figure  B-6.
Mean Annual Potential Direct Runoff in Inches.  Straight-row Corn
in Good Hydrologic Condition — Hydrologic Soil Group D.
Woolhiser  (1976)

-------
 (EPA/USDA,  1975) ,  which are  reproduced here as Table B-2 and  Figure  B-7,

 respectively.   In  the former,  the effectiveness of various practices in

 reducing runoff are qualitatively evaluated as "slight," "moderate,"

 and/or "substantial."  Figure  B-7 provides  a basis for obtaining  semi-

 quantitative estimates of f  values  from the indications provided by
                           R

 Table B-2.*  The  latter are interpreted considering the  characteristics

 of  the soil and any local experimental or monitoring data.  Woolhiser's

 simulations and hence this procedure are less reliable in  areas in which

 snowmelt is a dominant hydrologic factor (Woolhiser, 1975) .


     The subsurface drainage,  or  percolation rate is estimated by

 difference:
Estimates of q values are obtained  from  regional  streamflow records.   A

typical value for the Cornbelt  is  .25 m/year.



Phosphorus


     Phosphorus emissions are estimated  as the sums of three  separate

components:  extractable particulate, soluble, and soluble  phosphorus

leached from surface crop residues during snowmelt.  Only the NH^F/HCl

extractable portion of the particulate phosphorus (Bray P)  is included.
*    The reduction factors in Figure B-7 are related to mean growing  season
potential direct runoff, which can be estimated from mean annual potential
direct runoff by comparing the appropriate columns in Table B-l. The per-
centage reductions are assumed to be appropriate for both time  scales.
(See Table B-l.)

                                     188

-------
 Table B-2.  EFFECTS OF VARIOUS PRACTICES ON DIRECT RUNOFF  (EPA/USDA, 1975)
No.
R 1
R2
R3
R4
R5
R6
R7
R8
R9
R 10
R 11
R 12
R 13
R 14
R 15
R 16
R 17
R 18
Runoff Control Practice
No-till plant in prior crop residues
Conservation tillage
Sod-based rotations
Meadowless rotations
Winter cover crop
Improved soil fertility
Timing of field operations
Plow plant systems
Contouring
Graded rows
Contour strip cropping
Terraces
Grassed outlets
Ridge planting
Contour listing
Change in land use
Other practices
Contour furrows
Diversions
Drainage
Liindforininp
Construction of ponds
Practice Highlights
Variable effect on direct runoff from substantial reductions to
increases on soils subject to compaction.
Slight to substantial runoff reduction.
Substantial runoff reduction in sod year; slight to moderate
reduction in rowcrop year.
None to slight runoff reduction.
Slight runoff increase to moderate reduction.
Slight to substantial runoff reduction depending on existing
fertility level.
Slight runoff reduction.
Moderate runoff reduction.
Slight to moderate runoff reduction.
Slight to moderate runoff reduction.
Moderate to substantial runoff reduction.
Slight increase to substantial runoff reduction.
Slight runoff reduction.
Slight to substantial runoff reduction.
Moderate to substantial runoff reduction.
Moderate to substantial runoff reduction.
Moderate to substantial reduction.
No runoff reduction.
Increase to substantial decrease in surfiicc nmoff.
hiaiMxe to slij'.lit runoff reduction.
None to substantial runoff reduction. Relatively expensive.
Good pond sites must be available. May be considered as a
treatment device.
This is considered by some to be a measure of the "available" particulate




phosphorus (Romkens and Nelson, 1974).  The remaining inorganic and organic




particulate forms are assumed to be unavailable to support algal growth in




downstream impoundments.  Extractable and total particulate phosphorus data




from soils in the Black Creek area  (Sommers et al, 1975)  generally  support




Taylor's  (1967) suggestion that about ten percent of  the  phosphorus in  soils
                                     189

-------
 is available for aquatic plant  growth.  Other investigators have used other




 definitions of "available P"  which would correspond to lower percentages of




 total P (Porter, 1975).   This is  an  important assumption which is critical




 to evaluating the effects of  erosion controls on eutrophication and requires




 additional study.






      The  first step in estimating phoshorus emissions is to evaluate




 the extractable phosphorus content of  the  surface soil as a function of




   100 r






   90
   80
   70
I  60
o
•o
o>
c

OJ
o>
a.
   50
   40
   30
   20
    10
 Reduction Achieved  by Changing  from Row

 Crop to  Continuous Meadow

 (SCS Method)
Substantial Reduction Zone
 Moderate  Reduction Zone
                                Slight  Reduction  Zone
      01     23456789    10



            Mean Growing  Season1  Potential Direct  Runoff (inches)




          Figure B-7.   Definition of Ranges of Reduction in Mean

                       Growing Season Direct Runoff (EPA/USDA, 1975)
                                     190

-------
 fertilization rate,  tillage depth,  and baseline soil phosphorus levels.



 Direct measurements  of the extractable phosphorus contents of the vari-



 ous soil size fractions are relied  upon for model calibration.   The base-



 line,  average soil phosphorus level is computed from:

where





     P° = baseline, average phosphorus content of surface soil  (gP/kg soil),



     P  , PCT, PC  = baseline extractable phosphorus content of clay,
      \*Li   o X   oA


                     silt, and sand fractions in surface soil  (gP/kg



                     solid).




       The rates and depths of phosphorus addition to surface soils have



  been observed to influence the surface soil phosphorus content (Timmons,



  et al,  1973;  Brigham,  1977;  Rfimkens  and Nelson,  1974; Romkens, et al,



  1973).   A nearly linear relationship between the rate of fertilizer



  addition and the concentration of available phosphorus in surface soil



  has been reported by Romkens and Nelson (1974).   Timmons,  et al. (1973)



  detected increases of about .005 and .035 g available P/kg in eroded



  sediment from plots receiving equal  fertilizer doses which were plowed



  under and surface broadcast, respectively.  These increases are relative



  to unfertilized plots, the sediment  from which averaged about .010 gP/kg.



  By decreasing the depths of fertilizer incorporation, use of minimum



  tillage methods causes an increase in the surface soil phosphorus level,



  which tends to offset the benefits of such practices as means of con-



  trolling phosphorus losses through erosion  (Brigham, 1977).



                                     191

-------
     The  increase  in  surface  soil phosphorus over baseline  levels  due  to



 fertilization  and  tillage method is estimated  as follows:

where
     AP = increase in surface soil phosphorus  (gP/kg  soil)



      F  = fertilization rate (gP/m2 - yr)




      p  = surface soil density  (kg/m3)
      Z  = effective tillage depth  (m)




      K  = empirical parameter  (yr)
The empirical parameter K, accounts for removal and conversion of  fertilizer



phosphorus into unavailable forms .  The inverse of K, is a measure of  the
                                                    o


fraction of the added fertilizer phosphorus  which is recoverable  as avail-



able soil phosphorus.  Laboratory studies by Romkens and Nelson  (1974) have



given fractions  ranging from .25 to  .76 for various soil types.   A value



of .50 is assumed here, corresponding to a Kfi value of 2 year  .   Combined


                               3

with a p,, estimate of 1300 kg/m   (Buckman and Brady, 1960), this gives in-



creases of .037 and .005 gP/kg for minimum tillage (Z  = 1 inch =  .025m) and



conventional tillage (Z  = 7 inches = .18m), respectively, when a  typical


                            2
fertilization rate of 2 gP/m -yr is used.  These results are in line with



those of Timmons et al. (1973), as discussed above.



    With the increase in surface soil phosphorus level computed according



to the above scheme, corresponding increases in the phosphorus content of
                                   192

-------
each texture class are evaluated as follows:
                PS = P° + AP                                       (18)
                4
where

                 g
                P    =  surface soil phosphorus content  (gP/kg  soil)


          S   S   S
         P.,T ,P  ,P   =  phosphorus content of clay, silt, and sand  fractions


                        (gP/kg soil).


 The  load of sediment phosphorus  transported  downsteam to the impoundment is


 evaluated as  the sum over  the texture  classes:
            LP     =  (SD   PS   + S°   PS   + S°  PS )                   (22)
            LPSED    (SCL PCL + SSI  FSI  + bSA PSAJ  A                ^  '
where,


            LP     =  loading  of available phosphorus  in sediment
             jEjlj
                         2
                    (gP/m impoundment surface area-yr)


     The  second  component of phosphorus loading is the soluble fraction, which


is exported from  the  watershed in surface runoff and subsurface drainage:
            LPSOL  *  («R CR + %  V  A
                                    193

-------
where

                                                                          3
           C     = soluble phosphorus concentration in surface runoff (g/m )
            R
                                                                    3
           C     = soluble phosphorus concentration in drainage (g/m )


           LPom = loading of soluble phosphorus transported to the
             oOLi
                                   2
                   impoundment (g/m -yr) .


The runoff and drainage rates, q  and q , respectively, are estimated accord
                                K      D

ing to the methods described previously.  Soluble phosphorus concentrations


in surface runoff are computed from the average eroded sediment contents,


assuming a linear adsorption isotherm:



           CR=  f-                                               (24)
            R    Yp



            E    E   S     E   S     E   S                        (
           P  " XCL PCL + XSI PSI + XSA PSA                       (25)
where
                                                       3
              = phosphorus distribution coefficient  (m /kg)
           P  = average available phosphorus  content  of  eroded  sediment  (g/kg) .


Yp is a soil-specific parameter which  is evaluated based upon soil  available


phosphorus and soluble equilibrium  phosphorus concentrations  (Taylor  and


Kunishi, 1971) .  Based upon data from Romkens  and Nelson  (1974),  Yp  ranges  from

         3
.1 to 1m /kg for different soil types.  Data from the Black Creek  area

                                                   3
(Sommers et al, 1975) indicate a range of  .5  to 1 m /kg.


    Drainage is assumed to be in equilibrium  with relatively phosphorus-deficient

                                                                      3
subsoils.  Accordingly, C  is set at a relatively low value of  .03g/m .  This


is typical of soluble phosphorus concentrations in drainage from mostly  forested

watersheds in the Cornbelt, from which surface runoff is generally  insignifi-

cant  (Omernik, 1976).


                                    194

-------
    The final phosphorus export component is that which leaches from surface

crop residues  during snowmelt periods.  This component is soluble and is

considered separately because the phosphorus concentrations in snowmelt runoff

may not equilibrate with frozen surface soils.  The freezing, thawing, and

leaching cycle which culminates during initial snowmelt may release substan-

tial quantities of dissolved phosphorus from residues left on the soil surface

after fall harvest.  In studies of runoff from natural rainfall erosion plots,

Timmons et al (1968) found that more water-soluble phosphorus was lost in

snowmelt runoff from seedling alfalfa than from other periods or cropping

sequences studied  (continuous corn, rotation corn, and rotation oats).

Laboratory studies (Timmons et al, L970) revealed that dne freezing/thawing/

leaching cycle could release 9, 28, 6 and 5% of the total phosphorus  in

residues  from alfalfa,  bluegrass,  barley and  oats, respectively.   Three

consecutive  cycles released 36, 64, 13  and  16%  of  the  phosphorus  in  these

residues, respectively.   Timmons,  et  al  (1970)  estimated  potential emissions

under  field  conditions  based upon  the laboratory data  obtained  for  one  cycle

and  showed that  these amounts  could be  appreciable relative  to  other  soluble

phosphorus losses.   A major uncertainly  in  their estimates is  the  extent  to

which  snowmelt phosphorus concentrations may  equilibrate  with  (i.e.,  be

adsorbed  by)  partially  thawed  surface soils or  stream  bank sediments.*

     Despite  the  relative  lack  of data in this area,  inclusion  of  this com-

ponent is considered important for evaluating the  impacts of  tillage methods

on water  quality with regard  to eutrophication.  No-till  methods  tend to
*    Data from the Black Creek Watershed (Nelson, 1977) also indicate high
soluble inorganic phosphorus (SIP) concentrations in snowmelt.  At one
sampling station* for instance, the average SIP concentrations in 1976 snow-
melt was .19 g/m , compared with an annual average concentration of  .05 g/m  .


                                    195

-------
leave crop residues on the surface and thus create a greater potential for



leaching losses in snowmelt than conventional tillage methods, which in-



corporate residues into the soil.



    The following function is employed to estimate this component:
           LPPES= RESP (1 - FRES> K7                              (26>
Where
           LP__n  = impoundment phosphorus loading attributed to leaching
             RES

                                                            2
                    from crop residues during snowmelt (gP/m -yr)



           RES    = average mass of residue phosphorus on the soil surface


                                       2
                    after harvest (gP/m )



           ?_.„„   = fraction of residues plowed under for a given tillage
            •RES


                    method



           K_     = fraction of surface residue P leached in snowmelt  (year)'1.
A nominal value of 0.01 has been tentatively assumed for K  .  This value



is low, relative to the range assumed by Timmons, et al  (1970),  .05 to



.28.  A lower value is probably more appropriate, considering the possi-



bility of partial adsorption by surface soils and river bank sediments.



The nominal value has been assumed merely to demonstrate the potential



importance of this component of the available phosphorus losses  from



agricultural operations.  This, in turn, indicates a need for additional



data in order to permit a more quantitative definition of this component.





     The total phosphorus loading is evaluated as the sum of the sediment,



soluble, and snowmelt residue components:
                                     196

-------
     LPT - LPSED + ^SOL + LPRES                                     (27)
where




     LP  = available phosphorus load transported to the downstream



           impoundment  (g/m2-year).




This value is used to evaluate the water quality response in the impound-


ment with regard to transparency and chlorophyll-a.






Soluble Nitrogen




     Because nitrogen is generally more mobile in soil systems than



phosphorus, estimates of average soluble nitrogen export from agricul-



tural areas are based upon mass balances, rather than upon computed soil



erosion rates and adsorption chemistry.  Other investigators (Onishi, et al,



1974;  Tanji, et  al,  1977;  Harmeson,  et al,  1971)  have  employed  similar



models for the purpose  of  obtaining  rough estimates  of potential nitrogen



emissions.  A nitrogen  mass  balance  is assumed here  to consist  of  four



input  and  three  output  components:





           "FX + "FE +  *R  +  *M =  *Y  +  *D +  *L                       (28)




where
            N    =  fixation  rate  (gN/m2-year)
            FX



            N    =  fertilization  rate (gN/m2-year)
            FE
                                                    2
            N   =  rainfall  nitrogen input rate (gN/m -year)
                                    197

-------
            •                                   2
           N,,  = soil mineralization rate  (gN/m -year)
            •                    2
            N  = crop yield  (gN/m -year)


            •                             2
            N  = denitrification rate  (gN/m -year)


            •                                          2
            N  = total runoff and drainage losses  (gN/m -year)
The fixation component, N   , accounts for nitrogen fixation by leguminous
                         FX


crops and is estimated from the yield and nitrogen content of such crops


accounting for extra nitrogen fixed and contributed to the soil in the

                                                                   •
forms of residues and root exudates.  The fertilization component, N
                                                                    FE

is based upon the assumed fertilization rate.  The regional rainfall

                                                     n
component for northern Indiana is estimated at .3 gN/m -year  (MRI, 1976).


Mineralization accounts for the breakdown of soil organic nitrogen com-


pounds and the resultant net release of inorganic nitrogen forms.  This


is perhaps the most difficult of the input terms in the equation to



evaluate.  Onishi, et al (1974), have equated this component to the


nitrogen content of the crop yield obtained when no fertilizer is


applied.  A generalized nitrogen response curve for corn presented by


Lucas, et al  (1977), indicates that yields without fertilization are


about 45 percent of the yields obtained under optimal fertilization.


The N,, term is assumed to equal the nitrogen equivalent of this corn
     M


yield, less the precipitation input.



                                                              t
     On the other side of equation (28), the yield component, N , is


estimated from crop yield and assumed nitrogen content.  It includes


only the harvested product  (not the residues, which are assumed to be


returned to the soil).  The denitrification component, N , is estimated




                                   198

-------
as a fraction of the calculated net nitrogen input rate:
     ND= (NFX + NFE + NR + NM-VFD                              (29)
where
     F  = fraction of excess nitrogen which is denitrified
     F  is specified for each soil type; poorly drained soils have higher



values due to lower oxygen levels and lower leaching rates.  The  final



component, N  , accounts for soluble nitrogen losses and is evaluated by
            L


difference:
      NL = NFX + NFE + \ + NM - NY - ND                              (30)
 No distinctions are made between nitrogen losses in surface runoff and


 subsurface drainage.  Because of difficulties involved in estimating the


 denitrified fraction, estimates of nitrogen losses obtained in this way



 are probably better for relative comparisons of practices  (e.g., percen-



 tage differences) than as absolute levels.




      Nitrogen is assumed to be transported conservatively to the down-



 stream impoundment at the following rate:
      LN=NL
 where
      L  = impoundment nitrogen loading  (gN/m  -year)


                                    199

-------
This  scheme  ignores particulate nitrogen losses attributed to soil




erosion.  Sommers, et al  (1975), measured total and exchangeable nitro-




gen in  sediment from rainulator plots  in the Black Creek Watershed.  On




the average, only 1.2 percent and 5 percent of the total particulate




nitrogen was present as exchangeable ammonium in runoff from unferti-




lized and fertilized plots, respectively.  Due to sedimentation and to




the relative stability of particulate  organic nitrogen compounds, sedi-




ment  nitrogen would not be expected to represent an important source of




available nitrogen  (ammonium or nitrate) in downstream ecosystems, par-




ticularly when compared with soluble nitrogen sources calculated accord-




ing to  the above scheme.









Dissolved Color






      Estimates of dissolved color losses are required to provide partial




bases for estimating transparency and  chlorophyll-a levels in downstream




impoundments.  Of the components modeled in the watershed/impoundment




system, color is based upon the least  amount of data and/or established




principles.  The framework discussed below is quite theoretical and




should be considered tentative until data are located for calibration




and testing.






     The presence of color in natural waters has often been attributed




to humic acids of soil origin (Wetzel, 1975).  Estimates of dissolved




color in runoff are made here based upon computed sediment organic




matter content and assuming a linear adsorption isotherm between the




solid, organic matter phase and the dissolved color phase.  Following
                                 200

-------
the development for phosphorus,  the average surface soil organic matter




content is computed from the baseline organic matter contents of the




various soil size fractions:







     °0=XCL0CL+XSI0SI+XSA°SA
where
     O  = baseline organic matter content of surface soil (g/kg)




     0  , 0  , 0   = baseline organic matter contents of clay, silt,
      CL   SX   SA


                     and sand fractions  (g/kg)
Following equation (16), the increase in surface organic matter content




due to tillage depth and crop residue addition is estimated from:
          RES

     40 =
 where
      Ao = change in surface soil organic matter content (g/kg)




      RES  = residue organic matter returned to soil surface (g/m2-year)





      ZT = tillage depth (m)





      p  = soil density = 1300 kg/m3
       D




      K.. = an empirical parameter (year) ~
       o




 Inclusion of this term permits consideration of the enriching effects




 of minimum tillage methods on surface soil organic matter levels.  A




 Kg value of .5 year   has been assumed.   For continuous corn, this gives




 computed increases in •€>  ranging from 64 percent to 275 percent when





                                   201

-------
minimum tillage is used rather than conventional tillage in the various



Black Creek soils.  Residue organic matter and residue phosphorus are



assumed to be related by:
     RES  = 500 RES                                                  (32)
        o          p
This assumes that crop residues are .2 percent phosphorus, a typical



value for corn (USEPA/USDA, 1975).





     Assuming that the organic matter content of each size fraction is



increased proportionately, the average organic matter content of eroded



sediment is estimated as:
     oE - fxE  o°+xE  o°  + x°  o° )  n + — i                       mi
     °  ~ (XCL °CL XSI °SI + XSA °SA)  (1 + Qo>                       (33)




where

      TT

     O  = average organic matter  content  of  eroded sediment  (g/kg)•





In order to estimate  the concentration of dissolved color  in surface



runoff, a linear adsorption  isotherm is assumed:





     COR = 2!                                                       (34)
where
     CO  = dissolved color in surface runoff  (m"1);
       R



     Y  = organic matter/color distribution coefficient  (g/kgj/m"1
      c
Dissolved color is expressed here in units of the visible  light  extinc-



tion coefficient, meters'1.  Based upon the relationships  discussed in





                                   202

-------
the impoundment section, 1m"1 is approximately equivalent to 200 units



of Platinum-Cobalt color.  Independent data for estimating the distri-



bution coefficient, y ' have not been located.  For an assumed y  value
                     c                                          c


of 10 (g/kg)/m~  and typical field/watershed/impoundment characteristics,



computed values of CO  are within the apparent range of observed color
                     R


values for impoundments  (see Figure  C-5, Methods for Predicting



Impoundment Water Quality).  While this assumed value may be satisfac-



tory for a preliminary analysis, more data are needed to test the



assumed functional forms and parameter estimates for computing dissolved



color levels.





     The average color concentration entering the downstream impound-



ment is computed from:
      Cic  =
where
     C.  = average dissolved  color  level  in waters  entering the



           impoundment  (m~l).
This assumes  that the color content of  subsurface drainage  is negligible,



because  it  is in equilibrium with  lower soil horizons which are rela-



tively deficient in organic matter.







Calibration of Models for Practice Evaluations





     The models described above have been  calibrated for use on three



soil/field  types characteristic of the  Black Creek Watershed, Indiana.





                                   203

-------
Table B-3  summarizes the soil-specific parameter estimates and their


                                                   o
sources, most of which are self-explanatory.  The q  estimates for the
                                                   K.



various soil types are based upon the simulations performed by Woolhiser




(1976, 1977), as discussed previously.  Literature values of F , the




fraction of excess nitrogen which is denitrified/ range from .25 (Onishi,




et al, 1974) to .80  (Huber, et al, 1977).  Better drained soils would be




expected to have lower denitrification rates due to increased leaching




and increased soil aeration.  F  values of .5, .6, and .7 have been




assumed for the ridge, upland, and lowland soils, respectively.
     The models have also been calibrated for evaluation of eleven modes



of farm operation on each of the three soil types.  Parameter values are



summarized in Tables B-4, B-5, and B-6.     Each mode of farm operation



is defined by a rotation, tillage method, and terracing scheme.  Parame-



ter values represent the averages over the various crop rotations.





     Instead of adjusting L  (length of slope), P  (practice factor) is



used to adjust the gross erosion rate when a terracing system is



employed.  Installation of one terrace per field in practices 9 to 11



effectively reduces the length of slope by 1/2 and the gross erosion



rate by a factor of l/i/2~.





     Estimates of cropping factors have been obtained from a generalized




table in Volume I of U.S. EPA/USDA (1975).  According to Wischmeier and




Smith (1972), these values should be calculated for the Black Creek



region using the seasonal distributions of soil cover and rainfall



erosivity appropriate for the individual practices and for that region.
                                    204

-------
                                    Table B-3

                         FIELD/SOIL PARAMETER VALUES
SOIL TYPE
PARAMETER
Origin
Name
Texture
Hydrologic Soil Group
S
XCL
S
XSI
4
K
L
g
"n
p
po

o
o
*V
F
D
CD
0°
CL
°SI
0°
SA
EQUATION
-
-
(3)
(6)
*
(4)
(D
(2)
(2)
(16)
(16)

(16)

(14)
(24)
(29)
(23)
(30)
(30)
(30)
LOWLAND
lake plain
Hoytville
siltyclay
D
.43
.42
.15
.28
300.
.5
.166
.102

.049

.178
1.0
.7
.03
89.3
35.9
8.48
RIDGE
beach
Haskins
loam
B
.13
.44
.43
.37
300.
2.
.155
.036

.029

.064
1.0
.5
.03
88.1
14.2
3.32
UPLAND
glacial till
Morley
clayloam
C
.33
.44
.23
.43
300.
5.
.016
.011

.011

.127
.50
.6
.03
43.30
16.70
4.50
REFERENCE
a,b
a,b
a,b
d
a
a
a
f
e
a
b
b

b

g
a
g
e
b,c
b,c
b,c
a - Table 7.11, Sommers et al  (1975)
b - Table 7.18, Sommers et al  (1975)
c - Assuming Organic matter/total
    nitrogen = 20, MRI, (1976)
  d - SCS, USDA (1971)
  e - Assumed value
  f - SCS, USDA (1977) Figure 2.2 Lake
      and Morrison.
  g - Discussed in text.
205

-------
               Table B-4




Practice Parameter Values for Lowland Soil
Parameter
Equation
Practice*
1 CC-CV
2 CC-CH
3 CC-NT
4 CB-CV
5 CB-CH
6 CB-NT
7 CBWM
8 CBWM-NT
9 CC-CV-T
10 CC-CH-T
11 CB-NT-T
* CC
CB
CBWM
CV
CH
NT
T =
P C
(1) (1)
1.00 .42
1.00 .19
1.00 .11
1.00 .43
1.00 .24
1.00 .18
1.00 .068
1.00 .043
0.71 .42
0.71 .19
0.71 .18
ZT
(17)
.18
.09
.025
.18
.09
.025
.0
.025
.18
.09
.025
Fp Fj^g RESp fR
(17) (26) (26) (14)
1.96 1.0 1.57 0
1.96 0.5 1.57 0
1.96 0.0 1.25 0
1.22 1.0 1.02 0
1.22 0.5 1.00 0
1.22 0.0 .90 0
1.10 0.14 .65 .20
1.10 0.0 .63 .20
1.96 1.0 1.66 0
1.96 0.5 1.66 0
1.22 0.0 .96 0
Continuous Corn
Corn/Bean Rotation
Corn/Be an/Whe at/Me adow
Conventional
Chisel Plow
No-Till
Terraced
Rotation
Tillage, fall plow






                     206

-------
                              Table B-5




                Practice Parameter Values  for  Ridge  Soil
Parameter
Equation
Practice
1
2
3
4
5
6
7
8
9
10
11

CC-CV
CC-CH
CC-NT
CB-CV
CB-CH
CB-NT
CBWM
CBWM-NT
CC-CV-T
CC-CH-T
CB-NT-T
P
(1)
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
.71
.71
.71
C
(1)
.42
.19
.11
.43
.24
.18
.068
.043
.42
.19
.18
ZT
(17)
.18
.09
.025
.18
.09
.025
.04
.025
.18
.09
.025
FP
(17)
1.96
1.96
1.96
1.22
1.22
1.22
1.10
1.10
1.96
1.96
1.22
FRES
(26)
1.0
0.5
0.0
1.0
0.5
0.0
.14
0.0
1.0
0.5
0.0
RESp
(26)
1.57
1.57
1.57
1.02
1.02
1.01
.66
.66
1.66
1.66
1.07
fR
(14)
0.
.35
.70
0.
.35
.70
.65
.80
0.
.35
.70
For the purposes of this project, however, regionalization would have




little influence on the relative or absolute evaluations of the prac-




tices considered.






     z  values  of .18, .09, and .025 m have been assumed for conven-




tional (moldboard) plowing, chisel plowing, and no-till systems,




respectively.  While chisel plows may penetrate soils to the same




depths as moldboard plows, the fact that they incorporate roughly one




half of the surface crop residues suggests that they cover one half of





                                  207

-------
                            TABLE B-6





             Practice Parameter Values for Upland Soil
Parameter
Equation
Practice
1
2
3
4
5
6
7
8
9
10
11

CC-CV
CC-CH
CC-NT
CB-CV
CB-CH
CB-NT
CBWM
CBWM-NT
CC-CV-T
CC-CH-T
CB-NT-T
P
(1)
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
.71
.71
.71
C
(1)
.42
.19
.11
.43
.24
.18
.068
.043
.42
.19
.18
ZT
(17)
.18
.09
.025
.18
.09
.025
.040
.025
.18
.09
.025
FP
(17)
2
2
2
1
1
1
1
1
2
2
1
.15
.15
.15
.34
.34
.34
.21
.21
.15
.15
.34
FRES
(26)
1
0
0
1
0
0

0
1

0
.0
.5
.0
.0
.5
.0
.16
•
.0
.5
.0
RESp
(26)
1.28
1.28
1.21
.81
.81
.80
.55
.55
1.35
1.35
.85
fR
(14)
0.
.17
.35
0.
.17
.35
.40
.43
0.
.17
.35
the surface area.  Accordingly, an effective Z  value of .09 m




is assumed for chisel plowing.  For minimum tillage/ a value of .025 m




or 1 inch is assumed to represent the effects of natural mixing processes




in the soil (e.g., diffusion, earthworms, wind).  Practice 7 consists of




a corn-bean-wheat-meadow rotation, with minimum tillage, except for the




fall preceding corn, in which conventional tillage  is used.  The average




Z  value for this rotation has been selected so that Fp/Z  is equal to




the average ratio over the four-year rotation  (see  equation  (17)).





                                  208

-------
     The phosphorus in crop residues, RES , is estimated from the



assumed crop yields and residue phosphorus equivalents presented in



Table B-7. Conventional, chisel, and no-till systems are assumed to



incorporate 100 percent, 50 percent, and 0 percent of crop residues



into the soil after harvest, respectively.  The values of F    for
                                                           RES


Practices 7 and 8 have been selected so that computed values of RES



(1 - Fpgg) are equal to the respective averages of these products over



the four-year rotations (see equation (26)).





     The runoff reduction factors, f , are estimated for each soil type



and practice using the methodology described previously  (see Surface



Runoff and Percolation).  Soil types are important in determining the



response of runoff rate to tillage methods.  In soils subject to compac-



tion or with low internal permeability (e.g., lowland), minimum tillage



methods may not influence or actually cause increases in runoff rates



(Mannering, 1977).  In well-drained soils  (e.g., ridge) however, sub-



stantial runoff reduction can be expected when minimum tillage methods



are employed.  The  f  values in Tables B-4, B-5, and B-6 have been esti-



mated assuming that the ridge, upland, and lowland soils respond well,



moderately and not at all, respectively, to reduced tillage.




      The nitrogen budgets for all soil  groups and practices are summa-



 rized in Tables B-8,  B-9, and B-10.   The terms correspond to those in



 equation (28).   Nitrogen equivalents of crop yields have been estimated



 using the coefficients  in Table B-7.  Using the methods described pre-



 viously (see Soluble  Nitrogen), the mineralization term is estimated at



 4.2  gN/m -year.   For  a  typical soil organic nitrogen content of 120 g/kg
                                   209

-------
and a plow depth of seven inches, this mineralization rate corresponds
to a decay rate of about 1.5 percent per year, within the range of
reported values for soil humus, 1 to 4 percent per year (Buckman and Brady,
1960).  This rate is assumed to be constant for all row crops and soil
types evaluated.  In rotations, it is assumed to be zero during meadow
years.  The final columns in Tables B-8 through B-10 represent the net
nitrogen inputs, which are used, along with F  values, to estimate
soluble nitrogen losses in surface runoff and drainage.

     Some evidence of "ground truth" can be developed by comparing the
computed unit emission rates of various components with those measured
in streams draining the Black Creek Watershed.  Two automated stations
equipped for storm event sampling have been maintained on the watershed
by Purdue University since 1975.  The characteristics of the drainage


                                 Table B-7
               Assumed Crop Parameters  for Nitrogen Budget
                        and Residue Computations
Factor
Lbs. Yield P/bushel
Lbs. Yield N/bushel
Tons residue/bushel
Corn Bean Wheat
yield .16 .36 .28
yield .90 3.56 1.30
yield .030 .022 .030
Lbs. residue P/bushel yield .11 .089 .040
Haya
4.5
40.0
.18
.80
                  Hay  yield units in tons  instead" of bushels.
              b   USEPA/USDA Volume  1 (1975),
                                   210

-------
              Table B-8



Nitrogen Budgets for Lowland Soil
Practice
1
2
3
4
5
6
7
8
9
10
11



Term
(Equation (28)
2
) , (gN(m -year)
• • • • •
NFX NFE + NR + NM NY
CC-CV
CC-CH
CC-NT
CB-CV
CB-CH
CB-NT
CBWM
CBWM-NT
CC-CV- T
CC-CH-T
CB-NT-T
0.
0.
0.
8.
7.
6.
8.
8.
0.
0.
7.
0
0
0
38
60
82
91
91
0
0
21
17.
17.
19.
8.
8.
9.
4.
4.
17.
17.
9.
60
60
36
25
25
08
68
98
60
60
08
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
4
4
4
4
4
4
3
3
4
4
4
.20
.20
.20
.20
.20
.20
.15
.15
.20
.20
.20
12
12
10
14
13
12
12
12
13
13
13
.87
.87
.30
.60
.82
.35
.68
.51
.56
.56
.09
N +N
D L
9.23
9.23
13.56
6.53
6.53
8.05
4.66
4.83
8.54
8.54
7.70
 =  Nitrogen fixation rate (gN/m -yr)



 =  Nitrogen fertilization rate  (gN/m -yr)

                                         2
 =  Nitrogen input in precipitation  (gN/m -yr)



 =  Nitrogen input due to mineralization

    of soil organic N (gN/m -yr)



 =  Nitrogen removal in crop yield  (gN/m -yr)



 =  Net nitrogen excess = denitrification rate

                   2
    loss rate (gN/m -yr)
                   211

-------
areas above these stations are presented and compared with the charac-




teristics of the entire watershed in Table B-ll.  Nelson (1977) has pro-




vided preliminary data on the average flux rates of various components




at each station over each of two sampling years, 1975 and 1976.  For




each station, year,  and component, the contribution of septic tank




effluent estimated by Nelson has been subtracted from the reported total




flux.
                                Table  B-9




                    Nitrogen  Budget  for  Ridge  Soil
Term (Equation (28)), (gN/m2-year)
Practice
1
2
3
4
5
6
7
8
9
10
11

cc-cv
CC-CH
CC-NT
CB-CV
CB-CH
CB-NT
CBWM
CBWM-NT
CC-CV-T
CC-CH-T
CB-NT-T
•
0.0
0.0
0.0
8.38
8.38
7.99
9.49
9.49
0.0
0.0
8.38
* *FE +
17.60
17.60
19.36
8.25
8.25
9.08
4.68
4.98
17.60
17.60
9.08
*R *
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
•
4.20
4.20
4.20
4.20
4.20
4.20
3.15
3.15
4.20
4.20
4.20
•
^F ™
12.87
12.87
12.87
14.60
14.60
14.20
13.27
13.27
13.56
13.56
14.94
• •
9.23
9.23
10.99
6.53
6.53
7.37
4.35
4.35
8.54
8.54
7.02
                                 212

-------
     The ranges of observed fluxes are compared with the ranges of esti-




mated unit emission rates for various soil types and practices in Table




B-12.  The soil types include lowland (lake plain), ridge  (beach), and




upland (glacial till), while the practices include a corn-bean rotation




with conventional tillage (Practice 4 in Table B-4) and a corn-bean-wheat-




meadow rotation with minimum tillage, except for the year preceding corn




(Practice 7 in Table B-4).  The former is the dominant form of row crop-




ping in the watershed.  The two practices generally reflect the upper








                                  Table B-10




                     Nitrogen Budgets  for  Upland  Soil
2
Term (Equation (28)), (gN/m -year)
Practice
1 CC-CV
2 CC-CH
3 CC-NT
4 CB-CV
5 CB-CH
6 CB-NT
7 CBWM
8 CBWM-NT
9 CC-CV-T
10 CC-CH- T
11 CC-NT-T
V
0.0
0.0
0.0
6.42
6.42
5.84
7.87
7.87
0.0
0.0
6.23
*FE
13.75
13.75
15.13
6.33
6.33
6.96
3.72
3.92
13.75
13.75
6.96
\
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
*M
4.20
4.20
4.20
4.20
4.20
4.20
3.15
3.15
4.20
4.20
4.20
*Y
10.40
10.40
9.88
11.33
11.33
10.75
10.97
10.97
11.09
11.09
11.48
N +N
D L
7.85
7.85
9.75
5.92
5.92
6.55
4.07
4.27
7.16
7.16
6.21
                                    213

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                              Table B-ll




        Characteristics  of Drainage  Areas  Above  Sampling  Stations  in




                  the  Black Creek Watershed  (Nelson,  1977)

Area (hectares)
Soil Types
Lake Plain and Beach
(Lowland and Ridge)
Glacial Till (Upland)
Land Use
Row Crop
Small Grain and Pasture
Woods
Urban
Site 2
942
71%
29%
63%
26%
8%
3%
Location
Site 6 Entire Watershed
714
26%
74%
40%
44%
4%
12%
4950
64%
36%
58%
31%
6%
5%
and lower limits, respectively, of the computed flux rates for the




various practices evaluated on each soil type.






     As shown in Table B-12, year-to-year differences in the observed




fluxes are large.  It would be impossible to obtain reliable estimates




of the long-term average fluxes of these components based only upon data




from two years of sampling.  Because of this variability and because of




the distributions of land use, field characteristics, and cropping




practices in the watersheds, direct quantitative comparisons of the




observed and computed fluxes are not feasible.  The ranges of observed




fluxes in Table B-12 correspond at least send-quantitatively to the




ranges of calculated unit emissions rates for various soil types and




practices.





                                   214

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            TABLE B-12.   COMPARISONS OF OBSERVED AND ESTIMATED FLUXES OF VARIOUS COMPONENTS FROM THE BIACK CREEK WATERSHED
                                         Observed
                                                                                         Estimated
to
Component
Losses
(Jcg/ha-yr)
Delivered
Sediment
Soluble
Phosphorus
Available
Sediment
Phosphorus
Total
Phosphorus
Soluble
Nitrogen
Total Flow
(m/yr)
Surface
Runoff
(m/yr)
Site 2d Site 6d
1975 1976 1975 1976
2126 636 3725 353
.20 .05 .32 .07
.35a .06a .19b .02b
.55* .lla .51b .09b
21.0 6.1 15.2 2.8
(.296) (.12) (.26) (.10)
-
Range
Min. Max.
353 3725
.05 .32
.02 .35
.09 .55
2.8 21.0
(.10) (.29)
-
Lowland Ridge Upland
CB-CV CBWM CB-CV CBWM CB-CV CBWM
1104 188 2459 458 7553 1301
.27 .32 .10 .15 .08 .14
.16 .03 .21 .06 .13 .04
.43 .35 .31 .21 .21 .18
19.6 14.0 32.8 21.7 23.7 16.2
.25 .25 .25 .25 .25 .25
.18 .14 .06 .02 .13 .07
Range
Min. Max.
188 7553 !
.08 .32
.03 .21
.18 .43
14.0 32.8
.25 .25
.07 .18
             a    Assuming available sediment P/Total Sediment P = .069 ratio for  average soil type in subwatershed
             b    Assuming available sediment P/Total Sediment P = .044 ratio for  average soil type in subwatershed
             c    Septic tank contributions estimated by Nelson (1977)  have  been subtracted from the total  measured loadings.
             d    For site characteristics, see Table 11.
             e    Total flow measurements may not reflect all  of groundwater contributions
             f    CB-CV = corn-bean rotation with conventional tillage;   CBWM = corn-bean-wheat-meadow rotation  with minimum
                                                                               tillage  except year preceeding corn.

-------
      The  range of  computed  soluble  nitrogen export  (14  -  32.8  kg/ha-yr)

 appears to be  somewhat  high,  compared with  the  observed range  (2.8  to  21

 kg/ha-yr).   The extent  to which  all of the  groundwater  contributions are

 reflected in the reported measurements is unclear   however,  since some

 of the groundwater contributions may emerge further downstream in Black

 Creek or  in the Maumee  River.  Since groundwater is an  important trans-

 port  medium for nitrate, the  observed nitrogen  export values may be

 biased on the  low  side.  Alternatively, the assumed denitrification

 rates could be under-estimated,  or  soil nitrogen mineralization rates,

 over-estimated.


      While  the comparisons  in Table B-12 do not "verify"  the methodology

 or calibration,  they  suggest, minimally, that the estimates are not off

 by more than an order of magnitude.
 REFERENCES, APPENDIX B

Brigham, W. U.  "Phosphorus in the Aquatic Environment."  A Report to the
    Subcommittee on Fertilizers, Illinois Task Force on Agriculture, Non-
    Point Pollution, Urbana, Illinois, March 1977.

Buckman, H. O. and N. C. Brady.  The Nature and Properties of Soils.  The
    MacMillan Company, New York 1960.

Christensen, R. G. and C. D. Wilson ed.  Best Management Practices for Non-
    Point Source Pollution Control.  EPA-905/9-76-005, U.S. EPA, Region 5,
    Chicago, November 1976.

Foth, H. D. and L. M. Turk.  Fundamentals of Soil Science.  John Wiley and
    Sons, Fifth Edition 1972.

Harmeson, R. H., F. W. Sollo and T. E. Larson.  "The Nitrate Situation in
    Illinois."  Journal of the American Water Works Association, Vol. 63, No.
    5, May 1971, pp. 303-310.

                                    216

-------
Huber, D. M.,  H. L. Warren, D. W. Nelson and C. Y. Tsai.  "Nitrification In-
    hibitors - New Tools for Food Production."  Bio Science,  Vol.  27,  No.  8,
    August 1977, pp. 523-549.

Jones, L. A.,  N. E. Smeck and L.  P. Wilding.  "Quality of Water Discharged
    from Three Small Agronomic Watersheds in the Maumee River Basin."
    Journal of Environmental Quality, Vol. 6, No. 3, 1977.

Kilner,     "Enrichment of Clay and Surface Erosion."  Advances in Agronomy,
    1960.

Lake, J. and J. Morrison, eds.  Environmental Impact of Land Use on Water
    Quality.  Progress Report - Black Creek Project, Allen County, Indiana,
    Allen County Soil and Water Conservation District, EPA-905/9-75-006,
    November 1975.

Lucas, R. E.,  J. B. Holtman and L. J. Connor.  "Soil Carbon Dynamics and
    Cropping Practices."  From Lockeretz, W. ed.  Agriculture and Energy,
    Academic Press 1977.

Massey, H. F., M. L. Jackson and O. E. Hayes.  "Fertility Erosion on Two
    Wisconsin Soils."  Agronomy Journal, Vol. 45, 1953, pp. 543-547.

Midwest Research Institute.  "Loading Functions for Assessment of Water Pollu-
    tion from Non-Point Sources."  EPA-600/2-76-151, U.S. EPA, ORD, Washing-
    ton D. C., May 1976.

Nelson,  D. W.   Draft Tables  of Measured Nutrient and Sediment Export  Data
     from Black  Creek Watershed,  1975-1976.   Personal Communications,  Purdue
     University,  Department of Agronomy,  November 1977.

Olness,  A.  and  D.  L. Rausch.   "Callahan Reservoir:  III  Bottom Sediment  -
     Water-Phosphorus Relationships."  Transactions  of the ASAE, Vol.  20, No.  2,
     1977, pp. 291-300.

Omernik, J. M.   "The Influence of Land  Use  on Stream Nutrient Levels."  EPA-
     600/3-76-014,  Environmental  Research Laboratory,  Office  of Research and
     Development,  Corvallis Environmental Research Laboratory, January 1976.

Onishi,  H., A.  S.  Narayanan, T.  Takayama and E.  R.  Swanson.   "Economic  Evalua-
     tion of the Effect  of  Selected Crop Practices on Nonagricultural  Uses  of
     Water."  University of Illinois at  Urbana-Champaign,  Water Resources
     Center, UJLU-WRC-74-0079,  Research  Report No. 79,  March  1974.

Porter,  K.  S.   Nitrogen and  Phosphorus  - Food Production,  Waste and Environ-
     ment.  Ann  Arbor  Science, Michigan, 1975.

Mannering,  J. V.   Personal Communication, Agronomy  Department, Purdue Univer-
     sity, December 1977.
                                    217

-------
Rausch, D. L. and H. G. Heinemann.  "Controlling Reservoir Trap Efficiency."
   Transactions of ASAE, Vol. 18, 1975, pp. 1185-2113.

Romkens, M. J. M.  and D. W. Nelson.  "Phosphorus Relationships in Runoff from
    Fertilized Soils."  Journal of Environmental Quality, Vol. 3, No. 1, 1974,
    pp. 10-13.

Romkens, M. J. M.,  D. W. Nelson and J. V. Mannering.  "Nitrogen and Phospho-
    rus Composition of Surface Runoff as Affected by Tillage Method."  Journal
    of Environmental Quality, Vol. 2, No. 2, 1973, pp. 292-295.

Soiraners, L. E. et al.  "Section 7 - Water Quality Monitoring in Black Creek
    Watershed."  Environmental Impact on Land Use and Water Quality, Lake
    and Morrison eds., 1975.

Stall, J.  B.  "Effects of Sediment on Water Quality."  Journal of Environmen-
    tal Quality, Vol. 1, No. 4, 1972, pp. 353-360.

Stoltenberg, N. L.  and J. L. White.  "Selective Loss of Plant Nutrients by
    Erosion."  Proceedings of the Soil Science Society of America, 1953, pp.
    406-410.

Tanjii, K. K., M.  Fried and R. M. Van de Pul.  "A Steady - State Conceptual
    Nitrogen Model for Estimating Nitrogen Emissions from Cropped Lands."
    Journal of Environmental Quality, Vol. 6, No. 2, 1977, pp. 155-159.

Taylor, A. W.  "Phosphorus and Water Pollution."  Journal of Soil and Water
    Conservation,  Vol. 22, 1967, pp. 228-231.


Taylor, A. W.  and H.  M. Kunishi.   "Phosphate Equilibria  on  Stream Sediment
    and Soil  in a Watershed  Draining  an  Agricultural  Region."  Journal  of
    Agricultural and  Food Chemistry, Vol.  19, No. 5,  1971,  pp. 827-831.

Timmons,  D.  R., R. E. Burwell  and R.  F.  Holt.   "Nitrogen and  Phosphorus in
    Surface  Runoff  from Agricultural  Land as Influenced  by  Placement of
    Broadcast  Fertilizer."   Water Resources Research, Vol.  9,  No. 3, June
    1973, pp.  658-667.

Timmons,  D.  R., R. F. Holt and J.  J. Latterell.   "Leaching  of Crop  Residues
    as a  Source of Nutrients in  Surface  Runoff Water."   Water Resources
    Research,  Vol. 6, No.  3, October  1970, pp.  1367-1375.

Timmons,  D.  R., R. E. Brunwell and R.  F.  Holt.   "Loss of Crop Nutrients
    Through  Runoff."  Minn.  Science, Vol.  24, No. 4,  1968,  pp. 16-18.

U.S. Department of Agriculture.   Present and Prospective Technology for Pre-
    dicting  Sediment  Yields  and Sources.  Proceedings of Sediment Yield Work-
    shop, USDA Soil Lab., Oxford,  Miss.,  November 28-30,  1972, ARS-S-40,
    ARS,  USDA, June 1975.
                                    218

-------
U.S. Department of Agriculture,   Soil Conservation Service.   "National Engi-
    neering Handbook, Section 4, Hydrology."  U.S. Government Printiny Office,
    Washington D. C., 1971.

U.S. Department of Agriculture,   Soil Conservation Service.  "Technical Guide"
    Section III - A-2.5, Fort Wayne Field Office, Indiana, August 1977.

U.S. Environmental Protection Agency and U.S. Department of Agriculture.
    Control of Water Pollution from Cropland, Vol. I - A Manual for Guideline
    Development.  EPA-600/2-75-026a, November 1975.

U.S. Environmental Protection Agency and U.S. Department of Agriculture.
    Control of Water Pollution from Cropland, Vol. II - An Overview, EPA-600/
    2-75-026b, June 1976.

Vanoni, V. A. ed.  Sedimentation Engineering.  ASCE, New York 1975, 745 pp.

Wischmeier, W. H,  "Use and Misuse of the Universal Soil Loss Equation."
    Journal of Soil and Water Conservation, Vol.  31, January-February 1976,
    pp. 5-9.

Wischmeier, W. H. and D. D. Smith.  Predicting Rainfall - Erosion Losses from
    Cropland East of the Rocky Mountains.  ARS, U.S. Department of Agricul-
    ture, Agriculture Handbook No. 282, 1972.

Woolhiser, D. A.  "Hydrologic Aspects of Non-Point Pollution."  USEPA/USDA,
    1976.
                                     219

-------
                             Appendix G

         Methods for Predicting Impoundment Water Quality

 Introduction



      The models described  below have been  developed  for  use  in  assessing

 the  impacts of  agricultural  practices on impoundment water quality.   They

 are  of  an  empirical nature and are designed to predict steady-state  con-

 ditions in impoundments with regard  to the following water quality com-

 ponents :

      (1)   sediment  concentrations and trapping rates;

      (2)   total  phosphorus concentrations  and trapping rates;

      (3)   total  nitrogen concentrations and trapping rates;

      (4)   mean  summer, Secchi  Disc transparencies; and

      (5)   mean  summer, epilimnetic chlorophyll-a concentrations.

 Models  are formulated for  each of the above components based upon theo-

 retical considerations and the results of  previous modeling efforts.

When possible, calibration is  achieved through a formal  parameter esti-

mation exercise, using an  appropriate data base.  Models are "verified"

based upon analyses of residuals, tests for parameter stability and/or

use of an  independent data base.  In other cases, parameter estimates

are derived from measurements or experiments described in the literature

and are therefore more subjective.  In applying  these models, sensitivity

analyses will help to identify which of the parameter estimates require


more detailed study and evaluation.
                                   220

-------
     The methods can be used to assess the sensitivities of the above




water quality components to annual average input rates, or loadings,




of the following substances:




     (1)  water;




     (2)  sediment  (sand, silt, and clay);




     (3)  phosphorus (total soluble and extractable particulate);




     (4)  nitrogen; and




     (5)  color (dissolved).




Additional independent variables of importance include:




     (6)  mean depth; and




     (7)  impoundment type (reservoir vs. natural lake).




A variety of other morphometric, hydrologic, and regional factors have




also been evaluated as possible independent variables, but have been




found to be of relatively minor importance, at least within the three-




state region in which the models have been calibrated  (Ohio, Indiana,




and Illinois).  Due to the empirical nature of the models, use outside




of this region is not suggested, unless recalibration can be achieved




using an appropriate data base.  Some submodels and parameter estimates




are more theoretically based than others and may be more transferable




to other regions.  The pathways in the impoundment water quality analy-




sis are summarized in Figure C-l.









Data Base






The primary data base used in this effort is compiled in the attached




tables.  The EPA's National Eutrophication Survey  (1976) has provided
                                  221

-------
         LOADINGS
                             TRAPPING/
                             DECAY RATES
                           OUTFLOW/
                           EPILIMNETIC
                           CONCENTRATIONS
        Color
                            •*• Color
to
to
to
        Sediment
Phosphorus
        Nitrogen
                              Sediment
Phosphorus
                              Nitrogen
                            Color
                          -*• Suspended
                            Solids
                            IMPOUNDMENT  MORPHOMETRlC
                            AND HYDROLOGIC  CHARACTERISTICS
                                                                                  Transparency
                                                                                  Chlorophyll-a
                                                                                  Concentration
                           Figure C-l.  Pathways in Predicting Impoundment Water Quality

-------
the following types of information for each of fifty impoundments in

the Ohio-Indiana-Illinois region:

      (1)  location  (state, latitude, longitude);

      (2)  hydrology (average outflow rate);

      (3)  morphometry  (volume, surface area, drainage area, mean depth,
                       maximum depth);

      (4)  total nitrogen and total phosphorous budgets  (annual input,
                       output, and retention rates); and

      (5)  trophic state indicators (mean summer chlorophyll-a and
                       transparency).


     The National Eutrophication Survey  (NES) included a total of 75

impoundments in this region.  The remaining 25 have been excluded from

the study for one or more of the following reasons:

      (1)  nutrient and/or hydrologic budgets were either not determined
          or acknowledged by the NES as uncertain due to incomplete
          tributary and point source sampling program designs;

      (2)  mean depths were less than one meter;

      (3)  mean hydraulic residence times were less than 3 days;

      (4)  surface overflow rates were greater than 150 m/year; and/or

      (5)  other, unusual factors may have influenced nutrient dynamics;
          (e.g., Lake Sangchris Illinois has not been included because
          it is mixed via power plant cooling operations).

An additional data set of 20 impoundments has been compiled from those

rejected above and from NES impoundments in Iowa.  These data, considered

of lower quality, have been used as a partial basis for verification of

the chlorophyll model.


     Sedimentation rate data for fifteen of these impoundments have also

been obtained primarily from the USDA (1969).  Additional sources of water
                                    223

-------
 quality data, used for calibrating the optical component submodels, in-



 clude the U.S. Army Corps of Engineers (1977), Illinois State Water Sur-



 vey (1977), and the Indiana State Board of Health (1976).






 Sedimentation





      Curves developed empirically by Bruyne (1953)  are used to predict



 the sediment trapping efficiency of an impoundment  as a function of mean



 hydraulic residence time,  T (years).   The latter is  equivalent to Bruyne's



 "Capacity to Average Annual Inflow Ratio."  The trap efficiency, R   is
                                                                   s


 defined as the fraction of influent sediment which  is deposited within



 the impoundment:
      R  =  1  -   os
       s
               L.
                IS
where
     Rg  = trapping efficiency  (dimensionless) ;




      os = average sediment outflow rate  (kg/m2-yr) ;



     L.  = average sediment inflow rate  (kg/m -yr) .



Bruyne's original "envelope curves" characterizing  the R  vs.  T relation-
                                                        S


ship were based upon analysis of data from 38 impoundments.  These  curves



are shown in Figure C-2, along with the following algebraic form, which is



approximately equivalent :



            K T


     R  -
      8
               8
                                  224

-------
where
     T = mean hydraulic residence  time  (years)

     Kg = an empirical sediment decay rate parameter (year)
                                      -1
 This form essentially represents the trapping process as a first order

 decay reaction in  a completely mixed system,  characterized by a decay

 coefficient, K .   Figure 2 shows that Bruyne's median curve is approxi-
              s

 mately equivalent  to a K  value of 68 year   or about .20 days  .  Agree-

 ment is reasonable for impoundments with T values greater than .003 years.

 1.0
                                        Bruyne's  Curves
                                         Upper Envelope
                                         Median
                                         Lower Envelope
                                    RS=KST/(1
                                    Ks=120 year"1
                                    Kss  68 year"1
                                    Ks=  50 year"1
   .001   .002
 .005   .01     .02     .05    .10   .20     .50
T = Mean  Hydraulic  Residence Time (years)
       Figure C-2.  Sediment Trapping Efficiency Relationships
                                 225

-------
     From a theoretical point of view, a better form would represent




sediment trapping as a first order settling process, in which case the




decay coefficient would represent an effective settling velocity  (m/year),




and the independent variable would be surface overflow rate  (m/year).




The effects of seasonal temperature variations, flow variations, non-




ideal settling behavior, particle size distribution, and particle size




changes due to flocculation would render it difficult, however, to select




an appropriate velocity based upon Stoke"s Law.  Bruyne's approach is




more approrpiate for use in this context because it has been empirically




verified.






     Bruyne's model is modified here to account for the variation of




trap efficiency with sediment texture or particle size.  Smaller parti-




cles are less efficiently trapped within an impoundment due to their




lower settling velocities.  This results in the clay fraction of suspen-




ded solids in impoundment outflows being higher than those in impound-




ment inflows.  Rausch and Heinemann  (1975) attributed much of the




observed variation in the trapping efficiency of Callahan Reservoir to




variations in the clay fraction of entering sediment.






     This effect is included by using a different decay rate parameter




for each sediment texture class (clay, silt, and sand).  Since clay




and silt generally comprise the bulk of sediment loadings, decay rate




parameters for clay (50 year"1) and silt (120 year"1) have been selected




to correspond with Bruyne's lower and upper envelope curves in Figure C-2,




respectively.  Essentially all influent sand would be expected to be
                                  226

-------
trapped.  Accordingly, an arbitrarily high value of 8000 year"1




has been assumed for the sand decay rate.






     Based upon mass balance considerations, the average suspended




solids concentration in an impoundment outflow can be estimated from:

     cos = ci
     C.  = L. /Q                                                  //n
      is    is *s                                                 <4J





where,






     C   = outflow suspended solids concentration  (kg/m );
      OS




     C.  = inflow suspended solids concentration  (kg/m );
      JLS




     Q   = surface overflow rate  (m/year).
      s




Both the trapping rates and suspended solids concentrations are deter-




mined as the sum of the respective values for all texture classes.
Phosphorus Trapping and Concentration






     Phosphorus is considered an  important water quality variable inso-




far as it may control the growth  of phytoplankton  in an impoundment.



The models for chlorophyll concentration and transparency developed in




subsequent sections rely upon predictions of Cop,  the average outflow




total phosophorus concentration.  CQp estimates are developed from




average inflow phosphorus concentrations and a retention model.  As in




the case of sediments, the retention model predicts the fraction of in-




fluent phosphorus which is trapped in the lake sediments as  a result of





                                  227

-------
various physical, chemical, and biological reactions occurring in the


water column.  (Dillon, 1974).  A retention model is formulated and


calibrated for Cornbelt impoundments below.



     A previous analysis of data from north central and northeastern


U.S. impoundments (Walker, 1977) suggested that a model of the follow-


ing form would be appropriate for predicting phosphorus retention co-


efficients:
       _ R  =
         R
          p   C.    1 + K T
               IP        P
     Kp =
where,


     R   = retention coefficient for total phosphorus  (dimensionless)
      P


     C   = average outflow total P concentration  (g/m  )
      op


     C.  = average inflow total P concentration  (g/m )



     K   = effective first order decay coefficient for total P  (year)



     Z   = mean depth  (m)


     Q   = surface overflow rate = Z/T (m/year)
      s


bg,bi,b2 = empirical parameters.



This essentially represents phosphorus trapping as a first order decay


process in a mixed system, with the decay rate allowed to vary with Qg


and z according to equation (6).  The latter dependences were included


to allow for possible effects of incomplete mixing or  other factors
                                    228

-------
related to depth and overflow rate.  Best estimates of the empirical

parameters b0, bj, and b2 for lakes north of 42° latitude suggested the

following model:
     Kp = -82  -2- '•55 = .82 T~'55                                (7)
       - R  "
                        4
              1 + .82 T
Equation (8) explained 78 percent of the variance in the reported re-

tention coefficient data for 105 impoundments (Walker, 1977).  Similar

models have been developed independently by Larsen and Mercier (1975)

and by Vollenweider (1976) , for lakes in the same latitude range.


   Figure C-3  demonstrates that the trapping efficiencies of most of

the impoundments in the Ohio-Indiana-Illinois region are considerably

higher than those predicted by equation  (8).  Accordingly, a more

general form of the above model has been tested for these impoundments;


                       !                                            (9)
      1 - R  = 	=	
          P           ai  S2   a3
             1+aOQs  Z Cip

An equivalent form of equation  (9) is appropriate for  a  log-linear

regression  analysis:
        R           a.  a_    a
             • •„«.lz SP
           p
      a  =1.986
       o
                                   229

-------
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                                               A    A
                                  A A
              .3 -
                                                                                  .824T-484

                                                                            P~H-.824T'4M
                                                                      •                           -
                                                                    Model for Northcentral
                                                                    and  Northeastern Impoundments
                                                                               Ohio, Indiana f• Natural Lakes

                                                                               and Illinois   U Reservoirs
                                      J_L
                           1
                                                                   J	L
1
                                                                                                          J—L_L
                                                                                                  4.0   6jO 8.0 10.
.02        .04   .06  .08 .10        .20       .40   .60 .80 1.0       2.0

                            T = Mean Hydraulic Residence Time (years)

    Figure C-3.  Relationship between Total Phosphorus Retention Coefficient and Mean Hydraulic Residence Tijne

-------
     a1 = -.309 ± .101




     a2 = .805 ± .240




     a3 = .621 ± .185




All coefficients are significant at the 95 percent confidence level,



but equation (10) explains only 36 percent of the variance in log



(R /(1-R )).  This is a low level of predictive ability, relative to



that demonstrated by equation (8) for northern lakes.  This suggests



that other factors may be controlling phosphorus trapping in Corn Belt



impoundments and/or that these data are of poor quality relative to



those used in developing equation (8).  The latter explanation is con-



sidered less likely, because the data bases for both models have been



derived primarily from the NES, in which consistent sampling program



designs and data handling procedures were maintained.





      In order to permit an assessment of the possible effects of sedi-



 mentation on phosphorus trapping, sedimentation rates for 15 of the



 NES lakes have been obtained from a national data summary published



 by the USDA (1969)  and from local studies by the Illinois State Water



 Survey (1977b) and the Army Corps of Engineers (1970).   Effects of sedi-



 mentation have been evaluated with a modified form of equation (10):
                     a,  a    a_  a_

                      *' %P \ 4
                                     2
 where, S  = sedimentation rate (kg/m -lake surface-year)




      a  = .246
       o
                                  231

-------
     a  = -.491 ± .180





     a  = .280 ± .328





     a  = .647 ± .309





     a  = 1.095 ± .302





For the fifteen impoundments tested, equation (11) explains 76 percent




of the variance in log.,0(R /(1-R )), a marked improvement over the




performance of equation (10).  All of the coefficients are significantly




different from zero, with the exception of the depth exponent, a_.




The relatively narrow range of mean depths in this subsample of lakes




(1.2-5 meters) may have been responsible, in part, for this lack of




significance.





     The apparent importance of sedimentation rate as a factor influ-




encing phosphorus trapping is indicated by the size of a  relative to




the other exponents.  Multicollinearity among the four factors tested




renders it difficult to establish the relative magnitude of the various




coefficients with much confidence, however.  The correlation matrix




of parameter estimates is presented below:
          1.00





           .31      1.00
     a3    .37       .19      1.00
          -.76      -.51      -.34      1.00
                                  232

-------
     The sedimentation coefficient, a ,  is most significantly corre-



lated with the overflow rate exponent, a  (r = -.76).  This is attri-



buted to S  and Q  both being dependent upon the ratio of drainage
          T      S


area to surface area.  The failure of Q  to explain much of the reten-



tion coefficient variance in the larger data set indicates that S



does have significant predictive capability, although the relative



magnitudes of the coefficients a1 and a. are somewhat difficult to
                                1      4


determine from these data.
     The measured sedimentation rates employed in the above regression



analysis primarily reflect external loadings of sediment from the



respective watersheds, as opposed to sediment generated within the



impoundments as a result of primary production and chemical precipita-


                                                     2
tion.  The reported S  values range from 3 to 71 kg/m year.  The maxi-



mum rate of net primary production for temperate, eutrophic lakes



reported in a data summary compiled by Wetzel (1975) corresponds to



about 1.5 kg organic matter/m -year.  Due to decay processes and re-



spiration in the food chain, a small fraction of net production is



usually sedimented.  Estimates for Lawrence and Mirror Lakes are on



the order of seven percent  (Wetzel, 1975).  Precipitation of calcium



carbonate would also contribute to measured sedimentation rates.



Alkalinity changes, induced by photosynthetic removal of CO2/ are on



the order of .5 kg CaCO /m2 year for eutrophic systems  (Vollenweider



 (1968), Otsuki, et al.  (1974)).  Thus the reported sedimentation rates



are assumed to result primarily from erosion in the respective water-



sheds.
                                   233

-------
     Modifications of the reported phosphorus retention coefficient


data have been made in order to improve the reliability of the para-


meter estimates.  The NES phosphorus loading estimates were based upon

monthly grab samples of lake tributaries.  It is doubtful that these

estimates reflect loadings of particulate phosphorus entering during


storm events.  In a study of the NES Non-Point Source Watersheds,


Omernik (1976) reported that an average of 41 percent of the total


phosphorus export from 96 agricultural watersheds (80 of which were in


the Corn Belt region) was in the ortho-phosphorus form.  This is in


contrast with data derived from continuous flow-weighted composite


sampling, which typically indicate less than 10 percent ortho-phosphorus

(Nelson, et al., 1976).  An attempt to account for unsampled, extract-

able, particulate phosphorus loading has been made for each of the


fifteen lakes according to the following:
     L1  = L  + L Y                                                 (12)
      p     p    s ps


                        L
     R'  = 1 -  (1 - R ) -2-                                         (13)
      P              P  L.
     L   =S(l+-i-)                                              (14)
      s     t     68T
where,
                                                              2
     L  ,L'  = reported and corrected phosphorus  loadings  (g/m -year)
      P  P


     R  ,R'  = reported and corrected phosphorus  retention coefficients
      p  p    (dimensionless)
                                  234

-------
                                                       2
        L   = estimated external sediment loading (kg/m -year)
         s




       Y    = assumed extractable phosphorus content of entering

              sediment = .08 g/kg.




Equation (14) estimates the external sediment loading, L ,  from the
                                                        S


reported trapping rate S ,  by employing Bruyne's trapping curve (Fig-



ure C-2).   The assumed value of Y   is based upon measurements of
                                  ps


extractable phosphorus contents of sediment measured in Black Creek



rainulator studies (Sommers, et al., 1973) and in four Missouri Valley



agricultural watersheds  (Schumann, et al, 1973).  This effort to correct



the phosphorus loadings and retention coefficients reported by the NES



is admittedly approximate, but is  considered preferable to using the



reported values directly.  The reported and corrected loadings and reten-



tion coefficients are listed in attached  tables.  Using the corrected



retention coefficient data, the parameters of equation  (11) have been



re-estimated:




     a0 = .419



     ax = -.757 ± .127



     a2 = .236 ± .222



     a3 = .077 ± .207



     ai» = 1-175 ± .205



Since 33, the exponent for C.  , is not signi'ficantly different from zero,



it has been excluded and the remaining parameters, re-estimated:



     a0 = .377



     ai = -.779 ± .109



     a2 = -222 ± .211
                                  235

-------
     33 = 0


     ait = 1.201 ± .186


With these parameter values, equation  (11) explains 86 percent of the


variance in the "corrected" log..  (R /(1-R )) values and 77 percent of


the variance in R , with a standard error of .09.  Despite its low
                 P

significance level, the depth coefficient (a,,) has been allowed to remain


because this lack of significance may be attributed to the relatively


narrow range of mean depths in the data base (1.2-5.0 meters).



     The apparent importance of sedimentation rate as a factor influ-


encing phosphorus retention is partially supported by theory  and


independent experimental evidence.  The adsorption of phosphorus by


soils and sediments has been studied extensively and is considered to


involve primarily the adsorption of iron and aluminum phosphate compounds


to clay particle surfaces  (Syers, et al, 1972).  Kunishi, et  al,  (1972)


have observed this adsorption process to be partially irreversible.


Under the anaerobic conditions typical of lake bottom sediments, iron


phosphate compounds are much more soluble and equilibrium may favor the


release of phosphorus into the water column.  The rate of release may


be severely limited, however, by kinetics (e.g., diffusion rates).


Apatite formulation in calcareous sediments represents a permanent


phosphorus sink (Stumm and Leckie, 1970).  The empirical evidence pre-


sented above suggests that external sediment loadings do contribute to


net phosphorus trapping efficiency.  Thus, release of dissolved phos-


phorus from these lake bottoms may be small relative to adsorption/


sedimentation rates despite the fact that dissolved oxygen concentra-
                                  236

-------
tions less than 1 g/m  were detected by the NES in the bottom waters


of seven out of the fifteen impoundments.   An important implication


is that particulate phosphorus loadings may have little effect on aver-


age epilimnetic or outflow phosphorus concentrations in these types of


impoundments.  In fact, reductions in soil erosion could conceivably


result in reductions in phosphorus trapping efficiencies and subse-


quent increases in average epilimnetic phosphorus levels.   These relation-


ships may not hold true for impoundments with greater mean depths, which


would have more pronounced stratification and greater potential for


phosphorus recycling through anaerobic bottom waters.




     Additional theoretical interpretations of these results are


possible with reference to the "settling velocity" model proposed by


Vollenweider (1969) and Chapra and Tarapchak (1976) to predict phos-


phorus retention coefficients:






     1 - % ' ^ - rrf/5;




where,



     U  = effective settling velocity for total phosphorus (m/yr).
      P

Vollenweider (1969) showed that a U  value of approximately 10 m/yr


was appropriate for a sample of northern temperate lakes.   Comparing


this formulation with equation (11) and the last set of regression


coefficients shows that the settling velocity for these 15 Corn Belt


impoundments can be estimated from:
                                   237

-------
     UP -
        « .377 Q .231Z.222  1.201                                (17)
                s          t
The relative magnitudes of the exponents suggest a dominant influence


of S , the sedimentation rate.



     While measured sedimentation rates were available for only 15 of


the  50 impoundments included  in this study, further indirect evidence


can  be presented for the  effect of S  on phosphorus settling velocity.


One  would expect lakes with large percentages of their drainage areas


impounded upstream to have relatively low sedimentation rates, because


of sediment  trapping upstream.  This, in turn,  should result in lower


phosphorus settling velocities, according to equation  (17).  Five such


lakes could  be  identified within the original set of fifty.  Table c-1


compares the measured phosphorus settling velocities  (equation  (18)) of


these lakes  with velocities measured in the lakes immediately upstream.


These data indicate a consistent decreasing trend in phosphorus set-


tling velocity  moving downstream in each watershed.  For example,


Witmer flows into Westler, and Westler, in turn, into Dallas.  The U
                                                                    P

values for these lakes are 16.0, 10.2, and 2.0  m/year, respectively.


In addition, James Lake,  the  only lake in the data set with a reportedly


negative phosphorus retention coefficient, has  a watershed, 87 percent


of which is  impounded upstream.  While alternative explanations are


possible, these data are  at least consistent with the theory that sedi-


mentation rates partially control phosphorus trapping in these impound-


ments .


                                  238

-------
      Equation (16)  is  considered rather tenuous for use as  a predictive


 tool,  because of  its relatively small  data base,  parameter  collinearity


 and  rather  empirical form.   In  applying the model to evaluate the  water


 quality  impacts of  agricultural practices,  a minimum value  of 3  kg/m2-year


 is assumed for Sfc,  since the relationship between phosphorous settling


 velocity and sedimentation rate has not been examined below this


 Sfc value-    Compilation of additional  data from other areas of the


 country  and testing some more theoretically formulated models would be


 worthwhile in the interest of further  defining the relationships among
                              Table  c-1


              Phosphorus Settling Velocities  in Lakes  and
             Reservoirs with Partially Impounded Watersheds
Lake or
Reservoir*
Witmer
Westler
Dallas
Webster
James Lake
Olin
Oliver
Shelbyville
Carlyle
NES
Number
349
346
326
345
330
338
339
315
297
Percent of Water-
shed Impounded
0
96
96
0
87
0
55
0
39
U **
P
(m/yr)
16.01
10.19
1.98
16.15
- 1.09
40.01
6.75
26.96
9.59
 *  Grouped moving downstream in each watershed (e.g. Witmer flows into
 Westler and, in turn, into Dallas)
**
 p
£3jT-   =  effective phosphorus settling velocity.
                                   239

-------
phosphorus retention, sedimentation rate, hydrology, and impoundment



morphometry.





     Average outflow phosphorous concentrations are estimated  from the



average  inflow concentrations and estimated retention coefficients



according to the following:
     CoP = CiP
where,
     C   = average outflow total phosphorus concentration  (g/M  )
      op
     The outflow concentration is a good indicator of typical lake con-
                                                                *• _^



centrations.  A regression analysis of data from the 23 natural lakes




in the data set suggests the following relationship:
     C   =  .935 C -1-062                                          (19)
      mp         op




       2

     {R  =  .921, SEE = .136}*
A similar analysis of data from 27 reservoiis yields the following:
     C   =  .605 C   *887
      mp         op
     {R2 =  .702, SEE =  .139}*
*     Coefficient  of determination and standard error of estimate,

respectively, referring to log... (C  ).
                              10   mp



                                   240

-------
where,
     C   = spatial and temporal median, summer total phosphorus concen-



     tration in the impoundment (g/m ).



Note that the slope of the relationship is less for reservoirs as com-



pared with natural lakes.  This could be due to differences in hydro-



dynamics, particularly effects of bottom-water withdrawals from some



reservoirs.
Nitrogen Trapping and Concentration





     Nitrogen is considered an important water quality variable for



two primary reasons.  High nitrate levels are of concern with regard to



drinking water quality/ because of the possible toxicity.  Secondly,



supplies of fixed nitrogen are also required to support most types of



algal growth.  The development of a predictive model for nitrogen concen-



tration is analagous to that described above in the case of phosphorus.





     The impoundments sampled by the NES in the region appear to be



significantly less efficient in trapping nitrogen than in trapping



phosphorus, as indicated by the following regression equations:
        R  = -.032 + .618 R         {R2 = .40, SEE = .17}          (21)
         n                 p
        U  =   945 U'506            {R  = *25' SEE = '51>*         (22)
         n    '      p
      log   statistics.
                                 241

-------
One explanation for this behavior is that nitrogen is supplied to these



impoundments well in excess of phosphorus, relative to biological require-



ments.  The ratio of geometric mean nitrogen to phosphorus loadings is



24, about three times that typical of algal biomass.  Limiting nutrient



bioassay studies conducted by the NES also indicate that the algae in



most of these impoundments are phosphorus, as opposed to nitrogen-limited,



given sufficient light.





     Fixation of nitrogen by blue-green algae might also be responsible,



in part, for relatively low nitrogen retention efficiency.  This phenomenon



is probably not very important in the context of the total nitrogen budgets,



however, since reported direct measurements of N  fixation in aquatic sys-


                            2
terns range from 0 to .4 gN/m  - year (Wetzel, 1975), whereas reported



external nitrogen loadings for the fifty impoundments examined here


                2                                      2
average 103 gN/m  - year and range from 3.3 to 597 gN/m  - year.  The



presence of high nitrate concentrations would also tend to suppress



nitrogen fixation activity (Wetzel, 1975).





     Another factor possibly tending to decrease nitrogen trapping



efficiency is that nitrate nitrogen is not significantly adsorbed by



sediments.  This would tend to reduce the importance of sedimentation



as a nitrogen removal mechanism, as compared with phosphorus, but may



be offset, to some degree, by denitrification.  This has been tested



empirically by performing a regression analysis of the nitrogen retention



data, using a model analogous to that employed for phosphorus  (Equation 11):
      Rn       n  cl   C2 „  C3  0 C4                               (23)

       F-=CoQs    Z    Cin    St
        n


                                  242

-------
      c    =   1.928
       o
           = -1.155 ±  .395




                .262 ±  .770




           =  -.625 ±  .716
      c,,   =    .447 ±  .672
       4
       {R   =    .56, SEE =  .565}
 The sedimentation rate exponent, c  , is not significantly different from



 zero, suggesting that nitrogen retention is not as strongly linked to



 sedimentation as is phosphorus retention.  Similar conclusions are



 reached when alternative forms of this model are estimated, deleting



 the other insignificant parameters  (c  and c ).



       The parameters of equation 23 have been re-estimated, setting



  c  = 0 and using a data base of 43 impoundments:*
       c   = .223
        o
       c   = -.445 ± .092
           = .351 ± .200




           = .862 ± .299




           = 0
       {R2 = .455, SEE = .343}
*     The retention coefficients of the seven impoundments with reported

value less than zero have been excluded in order to permit the regression

analyses to be performed on a logarithmic scale.



                                  243

-------
Analysis of residuals from this model suggests that R  values are under-



predicted slightly (by about .12) in six out of seven lakes with nitrogen




to phosphorus loading ratios less than 10.  This is evidence for possible



nitrogen limitation in a few of these impoundments and suggests that the



above model should not be employed under nitrogen-limited conditions.



Future development of this model might take into account the coupling



of nitrogen and phosphorus retention mechanisms.





     Average outflow total nitrogen concentrations can be estimated from




the average inflow concentrations and estimated retention coefficients



according to the following:
      Con  =  Cin(1-V                                            (24)
where,
      C    =  average  outflow total  nitrogen  concentration  (g/m )
With  the  retention parameter  estimates  listed  above,  Equation  24



explains  77 percent of  the  variance  in  log   C   ,  with a  standard
                                          10 on


error of  .10.   It is  assumed  that  C   is  a  reasonable indicator of
                                   on


average epilimnetic total nitrogen concentrations,  although  no data



are available  to substantiate this;  the NES measured  only  inorganic



nitrogen  concentrations within  the impoundments.
Transparency





     Transparency  is an  important water quality variable,  not  only for



aesthetic reasons  but also because  it  influences  the amount  of light




                                  244

-------
available for photosynthesis.  Light penetration is considered to be



an important factor regulating the die-off rates of coliform bacteria



in natural aquatic systems (Chamberlin, 1978).  Thus, increased transpar-



ency would also be expected to result in lower ambient levels of these



organisms.  Pathogenic bacteria may be similarly affected.





     The Secchi disc is commonly used to measure transparency in



impoundments.  It can be approximately related to the light extinction



coefficient in the water column with model of the following form  (Vol-



lenweider, 1974):




      Z e = k                                                     (25)
 where,
      Z   = Secchi disc transparency (m)
       s
      e   = visible light extinction coefficient (m  )



      k   = an empirical constant.
 Holmes (1970)  has suggested that a k value of 1.44 is appropriate for



 turbid,  coastal waters.  Poole and Atkins (1929) suggested a value of



 1.7.   Simultaneous Z  and e measurements performed by the Indiana State
                     s


 Board of Health (1976)  in eight impoundments have been analyzed to



 verify the use of Equation 25 with k = 1.66, the geometric mean value



 for the data set (Figure C-4). The possibility of a positive bias in



 this  relationship at high e values needs to be examined with additional



 data.
                                  245

-------
  to
  fc_
  
-------
     The extinction  coefficient,  e,  represents  the  fraction of visible



light energy absorbed per meter of depth,  according to  Beer's Law


 (Wetzel, 1975) :
     I

     Y~ = exp(-eZ)                                               (26)

      •o
where,
     I   = visible light intensity at depth  Z  (cal/cm hr)
      z                    *

                                                      2
     I   = visible light intensity at surface  (cal/cm hr)
The light extinction coefficient can be approximately represented as a


linear function of four components  (Lassiter, 1975):





     e = e,, + e^ + <=« + e^                                       (27)
where,




     e  = extinction coefficient attributed to water  (m  )
      w


     e  = extinction coefficient attributed to non-living, suspended
      O


             solids (m  )



     e_ = extinction coefficient attributed to algal biomass  (m   )
      B


     e  = extinction coefficient attributed to dissolved color  (m  )





The first term, e , is on the order of  .04 m   , corresponding to  the
                 W


maximum observed Secchi depth of about  40 m  (Wetzel, 1975), and is



relatively insignificant in the impoundments being studied here.  The



following linear relationships are used to estimate the remaining three



components:



                                  247

-------
      es = ksS                                                   (28)




      eB = kBB                                                   (29)




      ec = kcc                                                   (30)
 where,
      S  =  concentration of  non-algal  particulate material (g/m )



      B  =  concentration of  chlorophyll-a (g/m )



      C  =  concentration of  dissolved  color  (Pt-Cobalt  Units)



      k_,  k_, k   =  empirical  constants.
      o   B   C
 The  calibration  of  three  equations  is discussed below.





      Secchi depth and  suspended  solids measurements  taken by  the  Illinois



 State Water Survey  (1977) in the Fox Chain of Lakes,  Illinois, and by  the



 U.S.  Army Corps  of  Engineers  (1977) in five Indiana  and Ohio  impoundments



 have  been used to develop an estimate for k . Figure C-5 shows the relation-
                                           S


 ship  between suspended solids concentration and the  extinction coefficient



 (determined from reported Z  values and Equation 25) .. Average values were
                           O


 reported for each of the Fox Chain of Lakes.  Individual measurements



provided by the  USAGE have permitted division of the data from each



 impoundment into two, egual-sized groups, based upon solids concentra-



 tions.  The two  summary points shown for each impoundment represent the



median e and S values in each group.  The suspended  solids concentrations



reported in these studies represent both algal and non-algal particulate



materials.  It is assumed that the later dominate, since these data are



 in the range from 2 to 80 g/m , while algal biomass  levels would not be
                                  248

-------
expected to be much in excess of 5 g/m ,  assuming a maximum chlorophyll-a


concentration of 100 mg/m  .  The lines drawn in  Figure G-,5 correspond to a

                o
k value of .085 m /g - suspended solids.   Deviations of the data from


the lines are assumed to be attributed to variations in e + e , the
                                                 o Fox  Chain of Lakes,
                                                    Illinois
                                                 • Ohio and  Indiana
                                                    Reservoirs
                                                       i
          10      20      30      40      50      60      70

             S=Suspended Solids  Concentration  (g/m3)
 Figure C-5.  Relationship between Visible Light Extinction Coefficients
      and Suspended Solids Concentrations in Corn Belt Impoundments
                                  249

-------
water  and color extinction coefficients.  No independent color measure-



ments could be located to verify this assumption.




                                                    2
     Further support for use of a k  value of .085 m /g is obtained
                                   s


from the results of Shannon and Brezonik  (1972) who derived the



following relationship for Northcentral Florida lakes:
      -  = .003 C + .152 N                                         (31)
     z
      s
where,
     C = dissolved color  (Pt-Co Units)




     N = turbidity (JTU)






In terms of the extinction coefficient, equation  (31) is equivalent to:






     e = .005 C + .252 N                                          (32)






With reference to Equation 27, the first term is attributed to dissolved




materials  (e ), while the second is attributed to particulate materials




(e  + e ).  The average ratio of turbidity to suspended solids for the
  b    D




Fox Chain of Lakes is .32 JTU/(g/m ).  Thus, in terms of turbidity, a


                  2                                     _]_

k  value of .085 m /g is equivalent to .085/.32 = .266 m  /JTU, which
 S


agrees well with Shannon and Brezonik1s value of  .252 m  /JTU.  Possible




variability in k  attributed to different particle types and size dis-
                O


tributions (Lassiter, 1975) suggests that the assumed value of .085 m /g




may only be appropriate for lakes in the region and not for rivers.
                                   250

-------
     Calibration or verification of the  color  term,  e  , cannot be



achieved directly because no color data  have been  located for these



impoundments.  Shannon and Brezonik's results  (Equation 32) suggest a



kC value of  -005m  /(Pt-Co unit).  Color is assumed  here to represent



humic acids  derived from soil organic matter  (Wetzel,  1975).  The



method for predicting color loadings based upon computed runoff rates



and sediment organic matter content has  been described previously.



Within an impoundment, color can be expected to decay  as a result of



microbial degradation and adsorption/sedimentation processes.  The



removal of color is represented here as  a first-order  reaction, in a



model similar to that employed for sedimentation:
             c.
     c   = 	i£                                                  (33)

      oc   1+K T
              c
where,
     C   = average outflow color concentration  (Pt-Co units)




     C.  = average inflow color concentration (Pt-Co units)
      1C



     K   = decay rate  (year  )
Secchi depth and suspended solids data from the upstream and downstream



ends of Mississinewa Reservoir  (U.S. Army Corps of Engineers, 1977) have



been analyzed to develop an approximate estimate for K , the color decay
                                                      C


rate parameter.  For each station and sampling date, a color concentration
                                   251

-------
has been estimated by employing Equations 25, 27, 28, and 30 and the

parameter estimates derived above:
                     !i§§ - .085 S - .040
         £"V"£w  =  ZS	                        (34)
    C       k               .005
Over a three-year period, the flow-weighted average inflow and outflow

color concentrations have been computed as 766 and 347 Pt-Co-units,

respectively.  The mean hydraulic residence time over this period was

about .2 years.  With reference to Equation 33, these values are equi-

valent to a K  value of about 6 year
             c

     These data suggest that color is considerably more conservative

than suspended clay, the decay rate for which, according to Equation

2, is about 50 year  .  The apparent color decay rate is high, however,

compared with typical degradation rates of humus in soil systems, .01-.04

year   (Buckman and Brady, 1966).  This suggests that adsorption/

sedimentation may be the dominant color removal mechanism as discussed

by Otsuki and Wetzel (1974).  More data are needed in order to further

calibrate and verify the relationships developed above for color degra-

dation and its contribution to the extinction coefficient.


     The algal light extinction component e , is assumed to be propor-

tional to chlorophyll-a concentration, according to Equation 29.  Riley's

(1956) data from mixed, natural, marine algal populations suggest that
                                   252

-------
the proportionality constant k ,  varies somewhat with chlorophyll
                              B


concentration:
     k  = 8.8 + 5.4 B *33                                        (35)
      B




                                                       2
According to this equation k  decreases from 40 to 20 m /g as chloro-
                            B


phyll increases from .005 to .1 g/m .   Other investigators (Lorenzen



and Mitchell (1973), DiTorro, et al, (1975)) have assumed constant


                                                                 2
values of k  within the above range.  An average k  value of 30 m /g
           B                                      B


is assumed here, although additional data and analysis could permit



better definition of the quantitative relationship between chlorophyll-a



concentration and light extinction.
     The relationship between transparency and chlorophyll in the NES



impoundments is shown in Figure C-6. From Equations 25, 27, and 29, the



Secchi depth is given by:
             k          1.66
     Z_ =
      S   a+kB     a+30B                                    (36)
               B
     a  =
                                                                  (37)
Independent measures of the non-algal portion of the extinction coeffi-



cient, a, are not available for these impoundments.  Accordingly,



Equation 36 has been plotted in Figure C-6 fijr various assumed values of



a ranging from 0 to 3.  The locations of reservoirs on the plot relative
                                    253

-------
                                                              • Natural Lakes

                                                                Reservoirs
.004 J005 006 .008 .01
                              .02      03   .04  .05  .06
                     B = Chlorophyll -a (g/m3)
.08   .10
.20
Figure C-6.  Relationship between Secchi Depths and Chlorophyll-a Concentrations in
           Corn Belt Impoundments

-------
to natural lakes indicate the relative importance of non-algal suspended


solids and color in controlling light penetration in the former systems.




     To summarize, transparency is estimated according to the following


equation:
where,
                   V + kcc + V                               (38)
     k  = 1.66


     ew = .04 m"1
                2
     k  = .085 m /g suspended solids
      O
     k  = .005 m V?t Co Unit
              2
     k  = 30 m /g Chlorophyll-a
The three independent variables in this equation  (S, C, and B) are esti-


mated for average summer conditions.  Methods for estimating B are dis-


cussed in the next section.




     Methods for estimating annual average S and C values have been


discussed previously  (Equations 3 and 33).  Summer concentrations of


suspended solids and color would tend to  be considerably lower than


annual average values, due to lower input rates and  longer hydraulic


residence times in impoundments during the summer months.  Based upon
                                    255

-------
analysis of data from Mississinewa Reservoir, Indiana  (U.S. Army Corps


of Engineers, 1977), summer average color and non-algal suspended solids


concentrations are assumed to be one third of the respective annual,


flow-weighted-average outflow concentrations:
     S = C  /F                                                    (39)
          OS  CS
     C = C  /F                                                    (40)
          OC  CS
where,
     F   - 3.0
      cs
A factor of two might be explained rationally by the fact that mean summer


flows are about one-half the annual average value in this region.  This would


approximately double hydraulic residence times during the summer (unless im-


poundment is used for flood control)  and thus provide twice as much time for


sedimentation and decay process.  The additional reduction might be attributed


to lower inflow concentrations during the summer months.  Additional data and/or


analyses are required to test and improve upon these assumptions.






Chlorophyll-a




     Chlorophyll-a is a measure of phytoplankton densities in an impound-


ment.  Along with hypolimnetic dissolved oxygen, transparency, and nutri-


ent concentrations, summer chlorophyll-a is often used as an indicator


of trophic state.  In the interest of aesthetics, maintaining aerobic


conditions in the bottom waters of impoundments and ecosystem "health,"
                                    256

-------
as indicated by the species present and their diversity, high chlorophyll




concentrations are considered deterrents to water quality.   In the




interest of fish production, however, chlorophyll might be considered




beneficial in certain concentration ranges.






     The method developed below for predicting chlorophyll levels in




corn belt impoundments is based largely upon theoretical considerations




and is empirically calibrated and tested using data supplied by the NES.




A basic assumption is that the growth of algal populations in these




impoundments may be limited by light, phosphorus, and/or nitrogen




supplies.  The model is shown to have reasonable predictive capability,




despite the fact that other types of growth limitation  (in particular,




carbon) have been ignored.  Future improvements might be achieved by




considering the effects of such additional factors.  The model is




developed below by (1) considering the limiting effects of each factor




separately; (2) subsequently combining these effects;  (3) calibrating




empirically; and  (4)  presenting some evidence of verification.  A




preliminary error analysis and an interpretation of the results are




also presented.






     Light is a potentially important limiting factor, particularly in




the turbid and colored waters characteristic of impoundments in the




Oorn belt.  The effects of light limitation on algal production are




represented below using a model originally developed by Lorenzen and




Mitchell  (1973) and later modified by Sykes  (1975) and Walker  (1977).




The following simplified differential equation represents the growth




of algae  in the mixed surface layer of an  impoundment  (Lorenzen and







                                   257

-------
Mitchell, 1973).
     g-=  (y - 

     umax   Js            Ts
where
     I    = visible light intensity at depth Z and time of day t
      z ,t


                     2
              (cal/cm -hr)



                                                             2
     I  = saturation light intensity for algal specie  (cal/cm -hr)
            growth rate at optimal light intensity  (days  )



                                 258

-------
Variation of light intensity with depth is represented by Beer's Law:
                                                                 (43)
where,
                                                            2
     I    = surface light intensity at time of day t  (cal/cm -hr)
      o /t
     e    = extinction coefficient  (m  )
As noted previously, the extinction coefficient is a linear function of


algal density:
     e = a  + k B                                                (44)
Variation of surface light intensity with time-of-day is represented by


a cosine curve  (Vollenweider 1966):
     I    = .5 I     (1 + Cos    ) , -   < t <                     (45)
      o,t       o,m           A      ^        2
          = 0                     , otherwise




where,



                                                   2
     I    = surface light intensity at noon  (cal/cm -hr)
      o,m


     X = day length (hours)


     t = time from noon  (hours)




By integrating Equation 45 over one daily cycle, it can be shown that:
                                   259

-------
        n,
       ,m
            2 I


            - °                                                  (46)
where,
     _                                         2
     I  = total daily visible radiation  (cal/cm  - day)
With other nutrients present in excess, the steady-state, light-limited



algal density can be estimated by setting Equation 41 equal to zero, com-



bining with Equations 42 - 46, integrating over mixed depth Z  and over
                                                             6


one, 24-hour cycle, and solving for B:
           max

            -~~  '   ~                                          (47)
                     [-PC-;
t  =54    I    |«P(-r^exp (-EZJ) -«p(-^i)|dt     (48)
where,
     B  = light-limited biomass  (g Chl-a/m )
      L
     F  = Surface light depth-integral  (dimensionless)
     Z  = Epilimnion depth (m)
      e
For a totally absorbing surface layer  (eZ  :> 5),



the first term inside the integral of Equation 48 is essentially equal
                                  260

-------
 to one, and the  integral can be  evaluated numerically for the  following



 typical parameter values:





     IQ = 240 cal/cm -day           (McGauhey,  1968)






     A  =13.5 hours/day




                  2
     1=2 cal/cm -hr              (Parsons and Takahachi, 1973)
      5





 I  and A values have been selected for an average summer day at 40°



 latitude, assuming 75 percent of possible sunshine.  The I  value is at
                                                          5


 the lower end of the range of experimentally determined values and is



 thus appropriate for the shade-adapted algae which would be present



 under light-limited conditions.  Accordingly,  the F integral has been



 evaluated numerically to give:






     F = .862 e — = 1.32                                        (49)






 The value of this integral is rather insensitive to the assumed values



 of I  and I .
    o      s





     Another factor which needs to be evaluated in Equation 47 is



 U   /&.   Under light-limited conditions, the decay term, S, would be



 governed by algal respiration, which is generally on the order of 10 per-



cent of the maximum photosynthetic rate (Parsons and Takahachi, 1973).



Accordingly, \i   /& is assumed to be 10.  The  incremental light extinc-



tion coefficient due to algae, k_ has been estimated previously at 30
                                Dt
                                  261

-------
m /g Chl-a.  Substituting the above parameter estimates into Equation 47


gives the following result:
     B  -      
-------
     Zfch = thermocline depth  (m)




     Z = mean depth  (m)







Snodgrass  (1974) analyzed data  from  a number  of  northern  lakes  and




derived the following empirical relationship:






     Z  = 1.6 Z*                                                  (54)
Using Z, Z    , and Z   values derived  from July  temperature profiles
          lUciX       ufi




measured by ISBH  (1976) in eight  Indiana  impoundments,  Z  values  have
                                                        e



been calculated according to Equation  52  and compared with the predic-





tions of Equation 54.  Agreement  is reasonable,  except  for Z  < 3  meters





in which Equation 54 gives Z  values greater than Z.  Accordingly,  the




following empirical method is used to  estimate Z :
                                                 O
     Z  = Z,   Z < 3m
      e          —
     Z  = 1.6 Z'57,  Z > 3m
                                                                   (55)
This method is appropriate for early summer conditions and may be less




valid in reservoirs with unusual hydrodynamic characteristics.






     Estimates of a, the residual, or non-algal component of the extinc-




tion coefficient can be derived from simultaneous Secchi depth and




chlorophyll concentration measurements according to the following




version of Equation 36.
         1.66

         -	30 B                                               (56)
                                  263

-------
When non-algal suspended  solids and color measurements or estimates are



available, a, can be estimated independently of B according to the



following version of Equation 38.





     a =  .04 +  .085 S  +   .005 C                                  (57)





In the calibration work discussed subsequently, Equation 56 is employed



to derive a estimates from  Z  and B measurements in the NES impoundments.
                            S


When the model is used in a predictive mode, Equation 57 is employed to



permit estimation of a and  B  as a function of estimated suspended
                            L


solids and color concentrations.
     Equation 50 indicates that a values greater than 13.2/Z  will



prevent algal growth due to severe light limitation.  Examination of



data from the NES has revealed one impoundment, Lake Springfield, with



a relatively low computed B  value of .007 g Chi  /m .  The observed
                           L


mean chlorophyll-a concentration in this reservoir was .013 g Chl-a/m ,



almost twice the computed, maximum light-limited value.  Similarly,



Lake Lou Yaeger (in the verification data set) has a computed B  value
                                                               L


of -.060 g Chl-a/m  and an observed concentration of .011 g Chl-a/m .



While errors in the data could be responsible for this, it is probable



that Equation 50 is not valid as B  approaches zero.  Light limitation
                                  L


could not result in a complete absence of phytoplankton.  Due to in-



complete horizontal mixing, shallow bays and littoral areas could support



algal growth in a turbid impoundment, despite the fact that average



conditions in the epilimnion might not.   In calibrating and applying



the model, B  is allowed to assume a minimum value of .020 g Chl-a/m .
            L
                                 264

-------
This assumption influences the computed B  values of only two out of
                                         L

the fifty impoundments used to calibrate the model.
     The effects of phosphorus limitation upon algal production are esti-


mated based upon kinetic and stoichiometric considerations.  Employing


Monod kinetics, the equation for algal growth as a function of available


phosphorus concentration under optimal light and other nutritional con-


ditions is given by:
     ft • <         i     -« •
                     a  p
where,
     p  = available phosphorus concentration  (g P/m )
      3.



     K  = half-saturation constant for phosphorus uptake  (g P/m )
      P
This equation is analogous to Equation 41 for light limitation and


assumes that light is available at optimal levels for algal growth


during the day.  At the maximum, phosphorus-limited biomass level, the


available phosphorus concentration can be found by setting Equation 58


equal to zero and solving for P  :
                               a
     p  = K  /	\                                       (59)
      a    pi,   •"•"      '
                                   265

-------
      Under  these  conditions  it  is  assumed  that the rest of the phosphorus


 has been  taken  up by  the  algae:
          Pt  " Pa
          -
where,
     B  = maximum, phosphorus-limited biomass  (gChla/m  )



     y  = algal p requirement (gP/m  )



     p  = total phosphorus concentration  (gP/m  )
The following parameter values are assumed:
     K  =  .01 g P/m3    (DiToro et al., 1975)
      P
     pmaX/<5  = 10       (Parsons and Takahachi, 1973)
     y  = 1 gP/gChl-a   (DiToro, et al., 1975)
     A  =13.5 hours/day




Accordingly, Equation 60 can be evaluated as:
     Bp =  Pfc - .0022                                               (61)
Assuming that the median, summer total P concentrations reported by  the


NES are representative at p values, B_ can be linked to average outflow


P concentrations  using Equations 19 and 20 for natural lakes and
                                   266

-------
reservoirs, respectively.  These, in turn, can be related to average



inflow P concentrations and retention coefficients using Equations



17 and 18.





     The effects of nitrogen limitation on algal production are repre-



sented in an analogous fashion:
     n  = K   -                                          (62)
      a    n  ,    max
              24  6
                        -,
     BN =  (nt - na)/YN                                             (63)
where,
     n  = available nitrogen concentrations  (g N/m )
      Si




     K  = half-saturation consistent for nitrogen uptake  (gN/m  )





     n  = total nitrogen concentrations  (gN/m )
     Y  = algal n requirements  (gN/gChl-a)





     B,, = maximum, nitrogen-limited biomass  (g Chl-a/m )
      N
The following parameter values are assumed:
     K  = .01 g/m3
     y  = 7 gN/gChl-a    (Parsons and Takahachi, 1973)
Accordingly, B  is given by:
              N
                                  267

-------
     BN = (nfc - .0022)/7                                        (64)

This equation ignores the possible effects of nitrogen fixation by blue-
green algae and is therefore not valid under conditions in which that
phenomenon is important.  It is assumed that n  is related to average
outflow nitrogen concentration in a manner similar to that observed
in the case of phosphorus, although no data are available from the NES
to verify this.

     Given the above expressions for the maximum light-, phosphorus,-
and nitrogen-limited biomass levels, a means of estimating the effects
of simultaneous limitation by more than one factor is required.  A model
of the following general form is proposed for that purpose:


    ©••[<.• (y" •($•(#]
where,
     B = observed, mean summer chlorophyll-a concentration  (g/m )

     m, f ,  f  , f  , f  = empirical parameters.
One characteristic of the formulation is that, for m >  0, a relatively

low value of B /f  would cause the corresponding term to dominate the
             L  L
right side of the equation.   In that case, light would  be controlling

the biomass level.  Similarly, phosphorus or nitrogen could be con-
trolling.  The parameter m determines the extent to which more than
one factor can be simultaneously important in determining the biomass

                                268

-------
level.  As m increases, the relative magnitudes of the various limiting
factor terms become increasingly different, permitting only one term to
dominate at a time.  As m approaches zero, the factor terms become
increasingly similar and the model approaches a multiplicative one.  The
value of (f /B )  ,  for example, could be viewed as a measure of the
           L  L
resistance to algal growth attributed to light limitation.  In that
sense, with f  = 0, Equation 65 is equivalent to the formula for the
total resistance of an electrical circuit consisting of three resistors
connected in series.  The empirical parameters have been included to
permit calibration of the model and testing of the significance of
each term.

     Calibration of Equation 65 has been achieved by employing the
BMDP Nonlinear Regression Analysis Program, BMDP3 (Dixon, 1975).
Coefficients have been selected to minimize the sums of squares of
residuals, expressed as the differences between the observed and
estimated, transformed chlorophyll-a concentrations.  The following
transformation has been found to give normally distributed, homoscedastic
residuals:
     B  = -1.//B                                                  (66)
where,
     B  = transformed chlorophyll-a concentration,  (g Chl-a/m )
Optimal values of f  , f  , f  , and f  have been estimated  for various
                   O   L  P       N
assumed values of m, ranging from .125 to 2.5.   In addition, K ,  a

                                  269

-------
 parameter in the light limited biomass expression (Equation 51),  has



 been optimized.   Since the K  value given in Equation 51  was derived



 from a variety of theoretical  assumptions and "literature"  values of



 the parameters y   /6  and  I ,  both  of  which are  subject to  error, optimi-



 zation of this parameter is considered both desirable and permissable



 without sacrificing the theoretical basis of the model.






      Initial calibration runs  using data  from 50 impoundments have



 indicated that optimal values  f and f are not  significantly different



 from zero for any of the assumed values of m (.125,  .25,  .5,  1.0,  1.5,



 and 2.0).  With  these  parameters set equal to zero,  the value of  m



 which gives  the  smallest mean  squared  residual is 1.0.  Optimal coef-



 ficients  for this case are  as  follows:






      f  =  1.866  ± .149
      P




      f  =  1.363  ± .333
      Jj





      1^ =  .440  ± .052







With  these coefficient values,  Equation 65  explains  82.4 percent  of the



variance of  B  , with a standard error of  1.378.  Observations are plotted



against model predictions in Figure C-7.






     Three strategies have been employed to test the model:   (1) analy-




sis of residuals;  (2) tests for parameter stability; and  (3) tests on



an independent data set.  Results of these tests are discussed below.
                                  270

-------
to
     -3X)-

     -45

N    -6.0
T
 |  -7.51-
 o
 5  -9.0
            •o
            ? -12.0
            in
            8 -,,5
               -16.5
          -16.5
                                                                                                 A   A
                                                                                               AAA
                                                                           A     •  A
                                                                                        A  Reservoirs
                                                                                        •  Natural Lakes
                                   I
I
                                                                                         I
  I
                                -ISO
-13.5      -12 X)      -10.5      -9.0
     Estimated   Bt  =  - 1/VB",  (
                               -7.5
-6.0
-4.5
                                    Figure C-7.  Relationship between Observed and Estimated. Transformed
                                           Chlorophyll-a Concentrations in Corn Belt Impoundments
-3X)

-------
         The residuals of the model have been tested  for normality and

   plotted against a variety of regional, morphometric, hydrologic, and

   nutritional factors derived from the data in  the  attached tables

   While formal statistical tests for normality  have not been applied, a

   normal probability plot appears to be linear  (Figure C-8) . Examination

   of  other residuals plots has revealed a slight  negative bias (averaging

   about -.7 or one half of the standard error)  in the  ten impoundments

   with  hydraulic residence times less than .1 years.   This may indicate

   that  flushing is an important removal mechanism (compared with respira-

   tion, for example) in these impoundments.  Future versions of the model

           .«•+••••+• »»«*»•» • + •»«•*«• »••#•••••*•••«*•••» + •••«*••••*••»•*'•.••*•••§ + ••«•
      2.4  ;                                                                  ;
                                                                       *

      1.8
 E
 X_
 P
 E
 C
. t_.
 E
 D
      .I.Z
      .60
 N	
 O
 f   0.0
 M
  V  -.60
  >	
                                                  * *
                                                 **
                                             *
                                             **
                                           **
                                       ***
                                      **
                                  **
                              *   *
                            * *
  L
  U
  E
	=U2
                         *
                         *
     -1.8
     -2.4  *
                                             -             -    .       ».*t.t A «.«_»_.
              -2.5     -1.5      -.50       .50       1.5      2.5       3.5
          -3.0      -2.0      -1.0      0.0       1.0       2.0       3««>
                                                             • »».f «»
                                                               2.5
                                        RESIDUAL
   Figure C-8.  Normal Probability Plot of Residuals from Chlorophyll-a Model

                                       272

-------
could account for this by calculating 6 (Equations 41 and 58)  as a



partial function of residence time.  A plot of residuals against



longitude indicates a slight positive bias (again averaging about one



half of the standard error) in the seven impoundments east of the 83



meridian.  The source of this bias is unknown.  Aside from the apparent


biases discussed above (neither of which is statistically significant),  no



systematic   deviations have been detected in residuals plots.




     Tests of parameter stability have also been performed in order to



develop some evidence of model verification.  The data set has been



divided into two groups  (23 natural lakes and 27 reservoirs) and



optimal f  , f , and K  values have been estimated for each group and
         PL       L


for assumed m values of.5, 1.0, and 1.5.  An F test based upon residual



sums of squares  (Dixon, 1975) has been used to test for  significant



parameter variations across groups for each assumed value of m.  Computed



F statistics for assumed m values of  .5, 1.0, and 1.5 are 1.89,  .93,



and 1.01,  respectively, with 3 and 44 degrees of freedom.  At the



90% confidence level, an F ratio of 2.43 or higher would indicate



significant parameter variations across groups.  While this test is



only approximate in the  case of a  nonlinear model, the apparent  stability



in the parameters  is evidence  for  verification of the model and  further



justification for  the selection of an m value of  1.0, which resulted



in the lowest F  ratio.




     The model has also  been tested using  data from  20  other  NES



impoundments in  the Midwest, including  seven  from Illinois,  one from



Indiana, three from Ohio,  and  nine from Iowa  (listed in Attachment).




                                   273

-------
Some of the data are from impoundments which were omitted from the



calibration data set for one or more of the reasons listed previously



(see Data Base).  The computed standard error of B  estimates for these
                                                  "t


20 lakes is 2.58, considerably larger than the standard error in the



data base used for calibration, 1.38.  Examination of the residuals



reveals a strong negative bias (about three standard errors) in the



residuals from the three impoundments with overflow rates greater than



150 m/year or residence times less than three days (Charleston, Beach



City, and O'Shanghnessy).  This suggests that flushing may be an



important algal removal mechanism in these impoundments, as noted in



the residuals plots discussed above.  Another impoundment with a highly



negative residual, Lake Weematuk, was sampled only twice by the NES



during the summer of 1974.  The chlorophyll estimate for this impound-



ment is therefore less reliable than for the others.  Finally, outflow



phosphorus concentrations in Lake Aquabi were sampled only five times




by the NES, as compared with 12 or 13 samplings in the other NES




impoundments.  If, for the above reasons, these five impoundments are



rejected from the data set, the standard error of the chlorophyll model




reduces to 1.38, in agreement with that observed in the data base used



for calibrating the model.






     One potential problem with the parameter estimation procedure is




that estimates of the independent variable a, obtained from the NES



data according to Equation 56 are dependent upon observed Secchi disc



and chlorophyll values.  Thus, in the above estimation procedure, B



appears implicitly on both sides of equation.  It would have been






                                 274

-------
preferable to have derived a estimates from independent suspended



solids and color measurements, had these data been available.  This


                                                                2
procedure used for estimating a may have inflated the apparent R  of



the model and the significance of the B  term.  The correlation
                                       L


coefficient between a and B is .10, however, indicating that variations



in a are governed chiefly by variations in Secchi depth and are nearly



independent of B values.  This suggests that ot is chiefly a measure of



non-algal turbidity and color and is not very sensitive to errors in



chlorophyll estimates.  Variations in B , according to Equation 50, are
                                       L>


also governed mostly by the changes in Z , as opposed to changes in a.



Thus, the problems arising from use of this procedure may not be impor-



tant, although the model should be verified using a estimates derived



independently, should such data be available in the future.




      Using expected  value theory,  it  can  be shown that the coefficient



 of variation of a chlorophyll-a  estimate  derived  from this model  is



 given approximately  by:
      CVB = 2t    0   = 2.76  B                                     (67)
 where ,





     B  =  estimated  chlorophyll concentration (g/m )




     CV  =  coefficient of variation of  B
        B




     o    = standard error of  model =1.378
      e




 This equation does  not consider the effects of parameter errors,  which



 would be  important  only at extreme  values of the independent variables.




                                  275

-------
At the average B  value for the data set, the computed coefficient of




variation of B is .348.  This corresponds roughly to a 95 percent con-




fidence range of ± 70 percent in the B estimate,  a fairly wide error



margin.






     A preliminary error analysis has been performed in order to parti-




tion the observed error into model and.measurement error components.




An important measurement error component is that associated with esti-




mating mean summer chlorophyll-a concentrations from grab samples taken




by the NES generally on three dates for each impoundment.  This error




has been quantified by compiling and analyzing the spatially-averaged




chlorophyll data for each sampling data and impoundment.  The computed




average coefficient of variation of the mean chlorophyll estimates for




fifty impoundments is .30.  This can be compared with the model resid-




uals, which indicate an average coefficient of variation of .35, as




calculated above.  Thus, an appreciable portion of the observed error




can be attributed to sampling errors in the mean chlorophyll values due




to temporal averaging.  This does not include errors due to spatial




averaging.  Other types of measurement errors are associated with the




independent variables in the model, including phosphorus concentrations,




Secchi depths and epilimnion depths.  Any remaining error can be




attributed to the effects of factors not considered in the model.  Based




upon the above analysis, that component is probably small compared with




the measurement error component.  Thus, the actual model error is pre-




dicting chlorophyll values is probably considerably less than that com-




puted according to Equation 67.
                                  276

-------
     The insignificance of the nitrogen term in Equation 65 is not



surprising, in view of the excess nitrogen supplies in these impound-



ments, as discussed previously (see Nitrogen Trapping and Concentration).



The average value of B^ for the data set is .287 g Chl-a/m , compared



with average B  and B  values of .094 and .077 g Chl-a/m , respectively.
              LJ      P


Thus, nitrogen supplies for algal growth are about three and four times



in excess of light and phosphorus supplies, respectively.  It is possible,



however, that inclusion of the nitrogen term in Equation 65 could  b,e justified,



given data from impoundments with lower nitrogen concentrations.  In



applying the model to assess soil management practices, the nitrogen



term is tentatively included with an assumed value of f  equal to f




(1.866).




      The empirically optimal value of K  is .440 ± .052, identical to
                                        L


 the theoretically proposed value.  This is surprising, in view of the



 assumptions and literature parameter values which went into the



 theoretical estimate.  While other "theoretical" values of K  are per-
                                                             L


 haps equally justifiable, the agreement between the empirical and



 a-priori values of this parameter lends some strength to the validity



 of the model.





      One theoretical interpretation of these results is that f /B  and
                                                               L  L


 f /B  are measures of the resistance to algal growth due to light and



 phosphorus limitation, respectively. Figure G-9  plots these resistance



 values, using different symbols for reservoirs and natural lakes.  The



 dashed lines in Fig.  c-9 represent lines of equal biomass potential,



 computed as the inverse of the sum of the two resistance terms, accord-





                                   277

-------
400
       o.Biomass  .  	!
       0 Potential  "  rL+rP
                                                                       400
              rP=fp/Bp  = Phosphorus  Resistance, (m/g Chl-a)
      Figure C-9.  Relationship between Light Resitance and Phosphorus
           Resistance to Algal Growth in Corn Belt Impoundments
                                  278

-------
ing to Equation 65.  The potential ranges from about .003 g Chla/m  in



the marl lakes of Northern Indiana to about .100 g Chl-a/m  in Buckeye



Lake, Ohio.  The solid, diagonal lines represent different ratios of



light resistance to phosphorus resistance.  Most of the impoundments



fall below the main diagonal, where phosphorus is the dominant control-



ling factor.  Light appears to be more important in reservoirs than in



natural lakes, as indicated by the relative positions of these two



groups on the plot.  Higher turbidity, color, and phosphorus concen-



trations are typical of reservoirs in this data set.  All of these



characteristics could be related to the lower geometric mean hydraulic



residence time of these reservoirs (.24 years), as compared with natural



lakes (.46 years).  Due to increased trapping/decay of sediment, color,



and phosphorus, impoundments with higher residence  times would be



expected to be increasingly phosphorus-limited.




     The following equations summarize the predictive methodology



developed  for mean summer, epilmnetic chlorophyll-a concentrations:
     B  = p  - .0022                                             (68)
      P    t




     BN - (nfc - .0022)/7



        = -440   o_

      L    Z   " 30
     ^  = 1.866   1.866   1.363                                  (71)


     B      BP      BN     BL
In applying this model to evaluate the effects of soil management
                                  279

-------
 practices  on water  quality,  the  following  relationships  are also employed

 to  estimate the  independent  variables:
      Pt '  -778 Cop                                               (72)

      nt =  .778 Con                                               (73)


      Z  =  1.6 Z'    ,   Z  >  3m                                     (74)

        =  Z         ,   Z  <_  3m

      a  =  .04 +..085S + .005 C                                    (75)


      The numerical constant in Equations 72 and 73 represents the

geometric mean ratio of median, summer total phosphorus to mean

annual outflow phosphorus  in the fifty impoundments used for model

calibration.  It should be noted again that inclusion of a nitrogen

term has not been empirically verified, possibly because of the exces-

sive nitrogen supplies in the impoundments used for calibration.  Model

predictions under nitrogen-limited conditions are therefore considerably

less reliable than those made under phosphorus- and/or light- limited

conditions .
REFERENCES, APPENDIX  C

Bruyne, G. M.   "Trap  Efficiency  at Reservoirs."   Transactions of the Ameri-
     can Geophysical Union,  Vol.  34,  June  1953, pp.  407-418.

Buchman, H. 0.  and N. C. Brady.   The Nature  and  Properties of Soils.
     McMillan: New York  1960.

Chamberlin, C.  Ph.D  Thesis.   Division  of Applied Science.  Harvard Univer-
     sity,  1978.
                                   280

-------
Chapra, S. C. and S. J. Tarapchak.  "A chlorophyll-a model and its relat-
    ionship to phosphorus loading plots for lakes."  Water Resources Re-
    search , 12 (6), 1976, pp. 1260-1263.

Christensen, R. G. and C. D. Wilson.  Best Management Practices for Non-
    Point Source Pollution Control.  EPA-903/9-76-005.  A report on a
    seminar held in Rosemont, Illinois, November 16-17, 1976, Section 108(a)
    Program, Office of the Great Lakes Coordinator, U.S. Environmental
    Protection Agency, Chicago 1976.

Dillon, P. J.  "A Manual for Calculating the Capacity of a Lake for Develop-
    ment."  Limnology and Toxicity Section, Water Resources Branch, Ontario
    Ministry of the Environment, October, 1974.

DiToro, D. M., D. J. O'Connor, R. V. Thomann and R. P. Winifield.  Mathemat-
    ical Modelling of Phytoplankton in Lake Ontario, EPA 660/3-75-005, 1975.

Dixon, W. J. ed. BMPD-Biomedical Computer Programs.  University of California
    Press, 1975.

Holmes, R. W.  "The Secchi Disc in Turbid Coastal Waters."  Limnology and
    Oceanography, Vol. 15, No. 5, September 1970, pp. 688-694.

Illinois State Water Survey.  Sedimentation Measurements on Lakes Spring-
    field and Vermillion.  Urbana, Illinois 1977b.

Illinois State Water Survey and Illinois State Geological Survey.  Fox Chain
    of Lakes Investigation and Water Quality Management Plan.  Cooperative
    Resources Report, No. 5, 1977a.

Indiana State Board of Health.  Reports on Limnologic Investigation of Lakes
    Martin, Palestine, Sylvan, Waubee, Webster, Crooked, Long and Hamilton.
    Water Pollution Central Division, Biological Studies and Standards Sec-
    tion, Indiana 1976.

 Kunishi,  H.  M.  et al.   "Phospate Movement from an Agricultural Watershed
     During Two Rainfall Periods."  Journal of Agricultural  and Food Chemis-
     try,  Vol.  20,  No.  4, 1972,  pp.  900-905.

 Larsen,  O.  P.  and H.  T.  Mercier.   Lake Phosphorus Loading Graphs:  An Alter-
     native .   U.S.  Environmental Protection Agency,  National Eutrophication
     Survey,  Working Paper No.  174,  1975.

 Lassiter,  R.  R.   Modelling Dynamics of Biological and Chemical Components of
     Aquatic Ecosystems.   EPA-660/3-75-012,  National Environmental Research
     Centre,  U.S.  Environmental  Protection Agency, May 1975.

 Lehman,  J.  J.,  D.  B.  Bothin and G.  E.  Likens.   "The Assumptions and Ratio-
     nales  of a  Computer  Model of Phytoplankton Population Dynamics."  Limnol-
     ogy  and Oceanography,  Vol.  20,  No.  3,  pp.  343-364.
                                    281

-------
Lorenzen, M. W. and R. Mitchell.  "Theoretical Effects of Artificial Destrat-
    ification in Impoundments."  Environmental Science and Technology, Vol. 7
    No. 10, October 1973, pp. 939-944.

McGauhey, P. L.  Engineering Management of Water Quality.  McGraw-Hill, 1968.

Nelson, D. W. and L. E. Sommers.  "Nutrient Contributions to the Maumee River."
    Christensen and Wilson, 1976.  Best Management Practice for Non-Point
    Source Pollution Control.

Omernik, J. M.  The Influence of Land Use on Stream Nutrient Levels.  EPA-600/
    3-76-014, U.S. Environmental Protection Agency, ORD, ERL, Corvallis,
    Oregon, January 1976.

Outski, A. and R. G. Wetzel.  "Calcium and Total Alkalinity Budgets and Cal-
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    Hydrobiology, Vol. 73, No.  1, February 1974, pp. 14-30.

Parsons, T. R. and M. Takahachi.  Biological Oceanographic Processes.
    Permagon Press, 1973.

Poole, H. H. and W. R. G. Atkins.  "Photo-electric measurements of submarine
    illuminations throughout the year."  Journal of Marine Biology Associa-
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Rausch, D. L. and H. G. Heinemann.  "Controlling Reservoir Trap Efficiency."
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Riley, G. A.  "Oceanography of Long Island Sound, 1952-54. 2. Physical Ocean-
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    pp. 15-46.

Shannon, E. E. and P. L. Brezonik.  "Eutrophication Analysis: A Multivariate
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Schuman,  G.  E.,  R.  G.  Spomer and R.  F. Piest.   "Phosphorus Losses  from Four
    Agricultural Watersheds  on Missouri  Valley Loess."   Soil  Science  Society
    of American  Proceedings, Vol.  37,  1973, pp.  424-427.

Snodgrass, W. J.  A Predictive Model  for Phosphorus in  Lakes  Development  and
    Testing.  Ph.D  Thesis.   Department of Environmental Sciences and  Engi-
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Steele, R. J.  "Environmental Control of Photosynthesis in the  Sea."   Limn-
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Stumm, W. and J. A. Leckie.  "Phosphate  Exchange with Sediments: Its  Role in
    the Productivity of Surface  Waters."  Advances  in Water Pollution Re-
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                                    282

-------
Syers, J. K., R. F. Harris and D. E. Armstrong.  "Phosphorus Chemistry in
    Lake Sediments."  Journal of Environmental Quality, Vol. 2, No. 1, 1973,
    pp. 1-14.

Sykes, R. M.  "The Prediction of Lacustrine Trophic Status."  Civil Engi-
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U.S. Army Corps of Engineers.  Miscellaneous water quality data from Indiana
    and Ohio reservoirs, 1971-77, Louisville, Kentucky, 1977.

U.S. Army Corps of Engineers.  Sedimentation studies of Lake Carlyle, Illin-
    ois, 1970.

U.S. Department of Agriculture.  "Summary of Reservoir Sediment Deposition
    Surveys Made in the United States Through 1965."  Miscellaneous Publica-
    tion Number 1143, May 1969, 64 pp.

U.S. Environmental Protection Agency, National Eutrophication  Survey Series
    of Working Papers.  Corvallis Environmental Research Laboratory, - Las
    Vegas, Nevada, 1975-6.

Vollenweider, R. A.  "Advances in defining critical loading levels for phos-
    phorus in lake eutrophication."  Mem. Inst. Ital. Idrobiol., 33, 1967,
    pp. 53-83.

Vollenweider, R. A.  "Calculation Models of Photosynthesis - Depth Curves and
    Some Implications Regarding Day Rate Estimates in Primary  Production
    Measurements."  Primary Productivity in Aquatic Environments, edited by
    C. R. Goldman, University of California Press, 1966.

Vollenweider, R. A.  "Input-output models with special reference to the
    phosphorus loading concept in limnology.  "Schweiz. Z. Hydrol., 37.
    1975, pp. 53-84.

Vollenweider, R. A.  Manual of Methods for Measuring Primary Productivity in
    Aquatic Environments.  IBP Handbook, No. 12, Blackwell Scientific Publi-
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 Vollenweider,  R.  A.   "Moglichkeiten und Grenzen elementarer Modelle der
     Stoffbilanz von Seen."  Arch.  Hydrobiol.,  66, 1969, pp.  1-36.

 Vollenweider,  R.  A.   "Scientific fundamentals of the eutrophication of lakes
     and flowing waters, with particular reference to nitrogen and phosphorus
     as factors in eutrophication."  Tech.  Rep.  DAS/C81/68,  Organization of
     Economic Cooperation and Development,  Paris 1968,  182 pp.

 Walker, W.  W.   Some Analytical Methods Applied to Lake Water Quality Problems.
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     gan, 1977.

 Wetzel, R.  G.   Limnology.   W.  B. Saunders Co.,  Philadelphia,  1975.


                                     283

-------
                          ATTACHMENT TO APPENDIX C


                     Tables of Data Used in Calibrating

                       and Testing Impoundment Models
                        Key to Symbols Used in Data Tables


     ID=  u.S.E.P.A. National Eutrophication Survey Working Paper Number


   NAME=  Impoundment Name


  STATE=  Location


TFDPHIC=  Trophic State (EUTR= Eutrophic, MESO= Mesotrophic, OLIG= Oligotrophic)


   TYPE=  Impoundment Type (RES= Reservoir, NAT= Natural Lake)



   LATI=  Degrees, North Latitude


   LONG=  Degrees, West Longitude

                         2
     AS= Surface Area (km )

                                                          2
     AD= Drainage Area, excluding impoundment surface,  (km )


      Z= Mean Depth (m)


   ZMAX= Maximum Depth  (m)


      T= Mean Hydraulic Residence Time  (years)


     QS= Surface Overflow Rate (m/yr)

                                      2
     LP= Total Phosphorus Loading (g/m -yr)


     RP= Total Phosphorus Retention Coefficient  (dimensionless)


    CIP= Average Inflow Phosphorus Concentration  (g/m )


    COP= Average Outflow Phosphorus Concentration  (g/m  )


     UP= Phosphorus Settling Velocity (m/yr)


     LN= Total Nitrogen Loading  (g/m2-yr)



                                     284

-------
   RN= Total Nitrogen Retention Coefficient (dimensionless)


  CIN= Average Inflow Nitrogen Concentration (g/m )


  CON= Average Outflow Nitrogen Concentration (g/m )


   UN= Nitrogen Settling Velocity (m/yr)


 CHLA= Mean Summer Chlorophyll-a Concentration (mg/m )


ALPHA= Non-algal Portion of Visible Light Extinction Coefficient=


             - -°3CHLA (nrl)
 ZSEC= Mean Summer Secchi Depth (m)


 DOMN= Minimum Hypolimnetic Dissolved Oxygen Concentration (g/m )


  TPM= Median Summer Total Phosphorus (g/m )


  OPM= Median Summer Ortho- Phosphorus (g/m )


  INM= Median Summer Inorganic Nitrogen (g/m )

                               2
   ST= Sedimentation Rate (kg/m -yr)


   LS= Apparent Sediment Loading (kg/m2-yr)

                                                2
  LP' = "Corrected" Total Phosphorus Loading (g/m -yr)


  RP'= "Corrected" Total Phosphorus Retention Coefficient (dimensionless)


  UP' = "Corrected" Total Phosphorus Settling Velocity (m/yr)
                                     285

-------
                          Table C-A.   Data Used for Model Calibration
(O
00
ID
296
297
301
309
312
313
315
NAME
BLOOM 1NGTC1N
CARL VLE
CRAD ORCHARD
LONG
RACOON
RENO
SHELBVVILLE
317 5PRINGFIELO
318 STOREY
320 VERMILION
322 WONOER
323
324
325
H»-
328
330
332
333
334
337
338
340
342
344
346
347
3*8
349_
393
395
306
398
399
400
AOl_
402
403
406
4 OB
409
411

OASS
CATARACT
CROOKED
DALLAS v
GfclSt
HAMILTON
JAMES LAKE
LONG
MARSH
Ml SSI SSI ME WA
MORSE
QLIN
P I GEON
TIPPECANOE
WA WA SEE
WE BS T ER
W6STLER
WHITEWATER
W I NONA
WITMER
ATWOOO
OERL IN
BUCKEYE
CHARLES MILL
DEEPCREEK
DELAWARE
DILLON
HOLIDAY
HOOVER
MOSQUITO CR.e
PLEASANT HIL
ROCKFQRK
ST MARYS
MPAN
STD OEV
MINIMUM
MA X I CUM
STATE TROPHIC
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
ILL
INO
INO
INO
INO
INO
INO
INO
INO
INO
I MO
INO
INO
IND
INO
INO
INO
INO
INO
IND
CHIO
OHIO
OHIO
OHIO
fHIO
OHIO
OHIO
PHICL
CHIO
OHIO
CHIP
OHIO
OHIO
EUTR
EUTR
EUTP
EUTR
EUTR
EUTP
EUTR
EUTR
EUTR
EUTP
fUTR
EUTP
EUTR
ME SO
EUTR
EUTH
EUTR
EUTR
MESO
EUTP
EUTR
EUTR
ME,SO
EUTR.
EUTR
MESO
•IE SO
MESO
EUTR
EUTR
EUTR
EUTR
EUTR
EUTR
EUTR
EMI"
EUTR
EUTR
EUTP
EUTP
EUTP
FUTR
EUTR
EUTP
EUTR
TYPE
PFS
RES
R.ES
NAT
NAT
RES
RES
RES
RES
RES
RES
__8ES._
NAT
RES
NAT
NAT
«?es
NAT
NAT
J^AT
NAT
NAT
RES
NAT
RFS
RES
NAT
NAT
NAT
NAT
NAT
	 NAT
NAT
RES
NAT
NAT
RES
R£S
NAT
"*ES
RtS
RES
RES
NAT
RCS
RES
Res
RES
RES
NAT
LATI
40.650
30.670
37.720
32.800.
42.380
38 .550
3B.080
_3.9,5QO_
39.720
40. =30
40. 170
Al"!~220
39.480
41 .670
_4.J«.S5fl_
39.920
41.550
41.320
	 41_.3QQ_
41.980
41 .720
40.670
LONG
33.920
89.250
89.O80
_fl8i08Q_
88.1 30
89.080
89.97O
_BB.$3.0_
89.600
90.400
37.650
86.580
86.920
35.050
85. 420
85.950
84.920
85.730
_B5.Q30_
45.030
84.980
95.920
39.080 86.420
40.080 86.030
41.570 85.390
_*..!.. SflO 	 35..4QCL
41.640 34.950
41.330 85.770
41.400 85.700
41.320 85.670
41 .320
39.610
41.220
41 .530
40.540
41.000
39.920
— 4..Q.t_7.5.Q 	
39.720
4O.330
40.000
35.390
84.970
85.830
_.B5,Ai>0_
81 .250
81.080
82.500
83.250
33.170
82.0RO
33.930
41.100 82.730
4O.080 82.870
41.330 80.750
__» 4. fiJO 	 B2.. 12 0_
39.180 83.500
40.530 84.500
40.558 85. 566
1.094 2.431
37.720 80.750
42.380 90.4OO
40.710 8S.410
AS
1 .970
105.200
28. 190
._l_J.i350_
1 . O3O
3.010
76.490
1 7 . 1 30
0.530
2.830
2.95O
AO
178.0
6937.0
492.4
24ia.O
98.7
122.0
1224 .O
_£665.0_
664.1
17.7
771.6
249. O
5.690 7.7
5.660 7*6.3
3.250 27.6
	 L.LSQ 	 UU«JQ_
7.290 S52.0
3.250 39.6
1.140 144.8
	 4_.leO 	 1 19.6
0.370 175.7
0.230 3S.4
12.750 2070.0
7.54O 28. O
43.500
5.570
0.420
1 .500
O.250
3.110
12.380
2. 37O
0.360
0.810
2.270
0. B3O
6.230
8. 900
12.710
5.46O
5. 170
S.260
S. 360
	 .500
6.700 23.500
2.1OO 13.7OO
6.100 11.600
4.600 14.900
9.100 24.400
_J_0.4DQ 	 L6.50Q 	
4.700 8.200
4.900 14.000
1.900 4.000
1.700 9.400
5.000 10.400
3.}OO 9.40O
3.000 7.600
1.900 8.100
3.900 4.900
6.500 I7.6OO
2.7OO 6.100
5.100 12.100
3.000 7.500
5. OP* 13-942
2.323 8.510
1.200 3.700
12.200 37.500
....4.750 ..11 .300
T
0. 200
o. teo
0.79O
__0.030
0 .OPS
0.200
1 .250
_Qj36Q
0. 480
0.770
0.025
	 0. 150.
3.200
0.140
2.600
Q.9 10
0. 160
1 .800
0.220
0.036
0.120
0. 140
__fi.70Q
0.660
0. 150
1 . 100
2»30Q
0.047
0.410
3. 500
0.130.
0.074
0.260
0.8"»0
	 Q. 320.
0.49O
0.222
0. 640
0.120
0.063
0.025
0.280
0.490
0.960
_fl. 09.fr
0.390
1 .600
	 C.726
1. 170
0.025
6.700
*s
17. 241
15.000
3.797
4S.6*7
16.162
6.000
3.7fo
13.880
8.333
5.974
56.000
16.667
0.561
43.571
2.346
26.34,1
22. 50O
3.500
37.273
p,. 4 an
141 .£67
50.833
51 .429
8.030
31.333
10 .636
97. 872
27.561
I .014
16* 154
82.432
17.692
10.460
32.500
9.592
22.072
2.969
30.009
41 .667
52.3B1
120.000
13.929
13.265
?.812
50.000
13.077
1.B75
96 .918
30.237
0.563
141 .667
18.410

-------
                          Table C-A  (cont'd).   Data Used for Model Calibration
               ID NAME
                                   LP
                                          RP
                                                 CIP
                                                        COP
                                                                UP
                                                                        LN
                                                                               RN
                                                                                      CIN
                                                                                             CON
CD
-J
. 8.96 0-00M INGIflN 	
297 CARLVLE
301 CRAB ORCHARD
302 OECATUR
... 309 LONQ 	
312 RACOON
313 RENO
315 SHELBYVILLE
—3 1 7_SPR INGF1EL D 	
318 STOREY
320 VERMILION
322 WONDER
.... 323.B4SS 	
324 CATARACT
325 CROOKED
326 DALLAS
327 GE 1ST
328 HAMILTON
330 JAMES LAKE
331 L JAMES
332 LONG
2J3 MARSH
334 MISSISSINEWA
335 MA-XINKUCKEE
336 MQNROE
317 MOHSE
338 OLIN
339 OLIVER
	 3 » Q ,£\ GE DN 	
342 TIPPECANOE
344 WAWASEE
345 WEBSTER
346 WESTLER
147 WHITEWATER
34« MI NONA
349 WITHER
3?3 ATWCOO
395 BERLIN
396 BUCKEYE
397 CHARLES MILL
398 OEERCRBEK
399 DELAWARE
400 DILLON
401 GRAMT
402^ HO'.IOAt
403 HOOVER
406 MOSQUITO CR£
40B PLEASANT HIL
409 RPCKFOHK
411 ST MARYS
MEAN
STp pFV
MI NIMUM
MAXIMUM
MEDIAN
	 2 . 1 70 	 c . 3J 0 	 JO..J 26 	
3.0OO 0.390 0.2OO
2.820 0.780 0.743
9.150 0.260 0.196
_2 3 -.6.6 Q 	 _Q .6 20. 	 i.. 3QJ 	
1.170 0.280 0.195
0.970 0.500 0.258
4.120 0.660 0.297
_. 1 • ?.0g__0 .250 	 0, 204 	
2. IRQ 0.690 0.365
10.200 0.330 0.182
12.390 0.560 0.743
._ 0.090 	 0..44.0 	 O.I 60 	
5.650 0.430 0.130
0.240 0.790 0.102
1.530 0.070 0.058
2.880 C.400 0.128
0.320
1.300
0.340
29.540^
0.470
-O. 030
0.590
0.330
6.090 0.060
12.590 0.490
0.150 0.670
	 Ot2flQ_ 0.430
6.520
0.810
O. 160
	 8.32.Q_
1.1 OO
0.110
1 .040
4.390
3.200
0.800
2.730
1 .670
5.870
0.510
5.560
5.540
12.490
32.910
8.510
8.410
0.530
0.790
0.560
	 D.20Q_
O.290
0.640
0.500
B.I 10
0.640
0.400
C.330
	 Q.6.5Q_
0.760
0.1OO
0.170
0.200
0.420
C.510
0.150
0.800
1.740 0.650
0.320 0.440
3.510 0.210
2.050 0.710
0.490
5.067
	 Z.-Q46^_
0.090
32.910
2.455
0.310
0.442
	 0.225
-0.030
0.870
0.440
O.091
0.035
0.040
0.209
0.087 7.746 174. ICO -O. 1 OO
0.122 9.590 4G.2OO 0.10O
0.163 13.464 13.000 O.550
0.145 16.396 239.600 -O.19O
_tt • 4.94 	 29 . 665_1 1.4 . 8 Q 0 	 0 .S5O_
0.140 2.333 19.500 O.430
0.129 3.760 9.900 0.420
0.101 26.961 a£. 900 0.220
_fl*J5.3 	 2«_77fi 	 C2..9JU1 	 Q.J40_
0.113 13.297 43.500 O.280
0.122 27.582 388.000 0.070
0.327 21.212 82.900 0.410
_fl .090 	 0 .442 	 3 . 300 	 Q. 420_
0.074 32.870 1 83 . 1 OO 0.260
0.021 8.826 6.500 0. 54O
0.054 1.983 62.90O 0.330
O.O77 1S.OOO 91.4OO -O.O2O
0.048 3.104 12.00O 0.530
0.036 -1.086 8P.ROO 0.040
0.016 12.215 13.400 0.460
0.140 69.776 5
-------
                            Table  C-A  (cont'd) .   Data  Used  for Model Calibration
                               10 NAME
                                                    CHLA
                                                            ZSFC
                                                                     ALPH
                                                                             OOMN
                                                                                       TPM
                                                                                                0PM
                                                                                                        I MM
10
00
00
                                   »i>65	8A?J»Q_
                                                                                     0.050
                              301 CRAB ORCHARD
                              102 DECATUR
                              309 LONG	
                              312 RACOON
                              313 REND
                              315 SHELBYVILL6
                19.200
                      17.200

                      Ifr&l-
                      31.200
                      9O.500
                                11 •,
                               0.399
                               0.724
                               0.983
                    2.422
                    1 .875
                    1.914
                      las
                    3 .587
                    i.sae
                    1.173
                                                                                                      5.730
                                                                                                      1 .27C
                  4.000   O.OQ4    O.032   1.270
                  2.000   0.082    0.013   0.2OO
                  0.500   0.129    0.002   3.7SO
                   .200   0.704	0.398   1 . 1 9O	
                  1.200   O.I 06    C.020   0.310
                  2.300   0.071    0.012   0.21O
                  1.000   0.062    0.019   3.290
                  4.200	O . 1 08    O . 059   3. g7O
  324
  325
  326
  127
                                  CATARACT
                                  CROOKED
                                  DALLAS
                                      ST
MARSH
MISSISSINEWA
HAXIKKUCKEE
MONROE	
 ORSK
                                 _
                              395 OERLIN
                              396 BUCKEYE
                              397 CHARLES MILL
                             .398.
  399 DELAWARE
  400 DILLON
  401 GRANT
	402_HQL.IO*.r	
  403 HOOVCR
  406 MOSQUITO
  408 PLEASANT MIL
	4 09 ROCKfORK
  4"! 1 ST  MARTS

      »* AN

      MI NI MUM
      MA XI MUM
      MEDIAN
                         0.470
                         0.356
                    OQ _ O.JT26
                1O. 700   O.ft46
                5.5SO   £.203
                10. 10O   2.202
                    go
                f.SOO
                11.500
                4.660
                                                                                    _9«.Lflfi	9.,.Q5A.
                                                                                     0.072   0.021
         1.642
         O.S60
         0.451

-*m—-fc
 1.676
 3.749
                             0.200
                    2.597    0.600   0.109    0.05O
                    1.953    7.200   0.426    0.132
                     • » 03	8_,.Q40	Q ,Q4Q	fl.,
                              348 MI NONA
                              349 Wl TMFR
34.500
15.800
 5.400
	6.^95,0
56.200
 4.S70
 3.770
J1.2Q9.
 6.050
 5.000
II.500
                                                   P. Z go	I.JBSZ
               _1
                33.100
                II.200
                11.900
                6t»0fl
                15.500
               106.600
               67.1OO
                                -..
                                1.237
                                0.676
                                2.530
                           8 _ L.-554
                           0    0.
                                              0.011
                                              0.005
                                              0.014
                                              SUQ.09_
                                              0. 01B
                                              0.006
                                              0.005
                                              I. I 50
                                              0.055
                                              O. 029
           0.757
           1 .405
           1.516
           0.965.
           O.B/9
           0.254
           0.4*5
 	9.899.	0«J7_59_
  10.a4O   0.404
  Z7.400   0.475
  4O.5OO   0.3*8
  55*400	0.691.
  I3.OOO   0.945
  36.300   0.891
  22.900   1.O97
 -2e.iQ.QQ.	: :
  79.200   0.401
         .at.?**..
         0.421
         0.332
         0.602
         0.575.
         1 .200
         0.646
         0.738
         j.eza.
         1 .424
         0.937
         1 .722
         _l«Jlfl9-
         3.785
         2.673
         3.555
         .O...Z2L.
         1 .367
         O.794
         0.026
         -U28^
         I .760
                                                 0.400
                                                 0.0
                                                 _L*0_
                                                 0.0
                                                 0 .0
                                                 0.0
                                                 .0.300	O.O3J-
                                                 1.400   0.042
                                                 5.360   0.179
                                                 0
0.019
0.012
0.025
£«o.as-
O. O84
0.03S
0.035
 O. 009
 0. OO3
 0.004
-0«J>JL5_
 0.005
 0.003
 C.OOS
_0-i.913_
 O. 012
 C.Ot 1
 0.011
 0. 005
                                            0.006
                                            0.020
                             0     0.127    C.011
                             IQQ	Q . 048	0 » 0 3«
                                          0.600
                                          0.700
                                          2. BOO
                                         _O«0	
                                          O.?00
                                          3.400
                                          O. 500

                                         ^6* 8OO
                                                                                       086
                                                                                       163
                                                                                       I 13
                                                                                                      2.510
                                                                                                      4.7QO
                                                                                                      0.090
                                                     1.660
                                                     0.120
                                                     O.R30
                                                    JjQBQ	
                                                     0.720
                                                     1.030
                                                     0.190
                                                     1.920	
                                            0.270
                                            2.400
                                   0.003    0.220
                          fi • i> 25   O. 007    O.?30
 3.330
 1.460
 0.920
_L.i50_
 0.200
 0.210
 0. 700

 1^620
 1.250
 0.000
_0.24S	
 0.000
 0.380
 0.465
  28.MB   1.187    1.327   1.295
..30,373	0.949	Q.937	2115
   9.770   0.254    0.221   0.0
 186.6OO   3.7*9    3.705   Q.OOO
  17.350   0.861    1.076   0.200
                                   O.04O
                                   0.058
                                   O.036
                                    i067_
                                   0.148

                                   0.008
                                   _0.«.L12-
                                   0.009
                                   0.7O4
                                   0.060
         0.024    2.340
         0.037    1.500
         0.010    0.57O
           .a3»	0.^15	
         0.008    I.64O
         0.006    0.150
         0.010    0.4*i5
         Q».QJQ	8.700	
         O.014    0.200
                                                           0.002
                                                           0.398
                                                           0.01Z
                                                                                                        301
                                                                                                        2fi&
                                                                                                      0. 120
                                                                                                      5.730
                                                                                                      0.895

-------
                Table C-B.   Sedimimentation Data Used for Phosphorus Retention Model Calibration
CD

296
297
301
302
312
317
320
347
395
396
397
399
401
408
411




ID NAME
Bloomington
Carlyle
Crab Orchard
Decatur
Racoon
Springfield
Vermilion
Whitewater
Berlin
Buckeye
Charles Mill
Delaware
Grant
Pleasant Hil
St Marys
Mean
Std Dev
Minimum
Maximum
ST
12.860
14.500
10.180
19.270
4.444
7.180
23.300
32.570
71.030
3.020
12.390
13.610
13.190
16.520
6.000
17.338
16.675
3.020
71.030
LS
13.512
15.685
10.369
28.716
4.771
7.400
37.006
34.412
76.735
3.089
15.703
16.787
16.085
19.051
6.055
20.292
18.415
3.089
75.735
LP
2
3
2
9
1
1
10
3
5
0
5
12
8
3
0
4
3
0
.170
.000
.820
.150
.170
.700
.200
.280
.870
.510
.560
.490
.510
.510
.490
.695
.783
.490
12.490
LP'
3
4
3
11
1
2
13
6
11
0
6
13
9
5
0
6
4
0
13
.251
.255
.650
.447
.552
.292
.160
.033
.929
.757
.816
.833
.797
.034
.974
.319
.585
.757
.833
RP
0.310
0.390
0.780
0.260
0.280
0.250
0.330
0.640
0.760
0.100
0.170
0.420
0.150
0.210
0.310
0.357
0.211
0.100
0.780
RP'
0.539
0.570
0.830
0.409
0.457
0.444
0.481
0.804
0.882
0.394
0.323
0.746
0.262
0.449
0.653
0.531
0.185
0.262
0.882
UP
7.746
9.590
13.464
16.396
2.333
2.778
27.582
31.453
69.895
0.330
6.331
37.931
5.004
13.291
0.842
16.331
18.768
0.330
69.895
UP1
20.193
19.875
18.541
32.230
5.052
6.647
51.841
72.702
164.820
1.928
14.745
47.642
10.049
40.772
3.529
34.038
41.646
1.928
164.820

-------
                             Table C-C.   Data Used  for Model Testing
             IO NAME
                                 STATE  TROPHIC
                                                    TYPE
                                                             LATI
                                                                      LONG
                                                                                          AO
                                                                                                         7MAX
                                                                                                                     T,
                                                                                                                             QS
  295 BALDWIN
  299 CHARLESTON
  308 HORSEHOE
_ 3 14>_L°_U	Y-*.C GCR
  314 SANGCHRIS
  316 SLOCUM
  321 WEEMATUK
  341 J51LVAN	
"  304 BEACH ClTY
  407 OSHAUGHNF.SSY
  410 SHAW NfE
 _494 AHQUABJ	
  495 BIG  CP.RFK
  496 BLACKHAWK
  500 MACBOIOF.
_5 Ol_ P» ABI F_ROSE	
  503 PEO  ROCK
  504 ROCK CPEEK
  so7 viKING
 _5_0 5_ SI L.V EB	
                                           EUTR    NAT
                                          _«y»T.E	CES_
                                                           3B.220   89.87O   8.00O
                                                           39.470   89.150   1.450
                                                           38.700   90.080   e.78O
                                                           39, ZOO	&91.6QQ	5*-7.29_
                                                           39.630   89.470   10.930
                                                           42.260   BO.190
                                                           40.530   90.150
       4.L 1.48.0.	85,3.70.
       40.630   81 .500
       4O.16O   83.12O
       39.650   83.780
       41 ..260	93.590	0.530
       41.600   93.75O    3.440
       42.30O   95.040
       41.610   91.550
_RES	4J .-, MO	95.,
 PCS   41.430   93.07O
 RFS   41.150   92.850
 RES   40.980   95.030
RES
NAT
RFS
                                                                              3 . 7 2O
                                                                              3.840
                                         	EUIR._	NAT   43.4BQ	23*420-
                                                                               _.
                                                                             36.220
                                                                              2.600
                                                                              0.610
                MF AN
                STD OEV
                MA XI MUM
                MEDIAN
       40.617   90.139   4.981  1920.4
        1.356    4.123   7.91O  7O8S.6
      _3 6*730	9L..5M	O^SJQ	4.6
       43.48O   95.200  36.220 31680.0
       41.130   90.115   ?.S75    70.2
                                                             7.9OO
                                                             0.003
                                                             1. 150
                                                            Jl.330.
                                                             1 .200
                                                             0.330
                                                             0.450
3.100   12.800
O.900    1.500
2.100   -1.000
i.30_Q	6_,ZOO_
 .000   IO.OOO
 .200    1.500
 .000    6.100
_*30Q	IJ. ..0.0ft	OA-4.4.0
   ?00    3.000    0.008
   800   15.500    O.O2S
2.500    7.600    0.200
ia.000	ft. 600	_O.I20._
6.700   15.500    0.740
1.700    3.700    0.^40
7.300   14.200    2.200
_3j3QQ	S-> »-0	
3.000   10.700    0.027
2.7OO    6.700    0.390
5.800   12.5OO    1.600
_i_*24Q	x.aoo.
                                                                                                                        146.341
                                                                                                                        I92.0OO
                                                                                                                         12.500
                                                                                      3.195   7.695
                                                                                      1.932   4.941
                                                                                      0.9QO  -1.QQQ
                                                                                      7.300
                                                                                      3.000
                                                    15.500
                                                     7.150
                                                              1.043  41.402
                                                              1.719  81.732
                                                              Q.OO1   0.39?
                  7.900 300.000
                  0.585   4.OBJ
10
V0
O
                             Table  C-C  (cont'd).   Data Used  for Model  Testing
                 10  NAME
                                         LP
                                                  RP
                                                          CIP
                                                                   COP
                                                                            UP
                                                                                     LN
                                                                                                      CIN
                                                                                                               CON
                                                                                                                         UN
295__
299
308
310
31.4.
116
331
341
407
410
494
4 95
496
500
501
-5Q3-
504
507
505


BALDWIN
CHAPLFSTON
HORS6HOE
LOU YAFGER
SANGCHRI S
SLOCUM
WEEMATUK
SYLVAN
BG.AC.H Cl TY
OSMAUGHNeSSY
SHAW NEE
AHQUARI
Bl G CRfEK
0.580 0.980
52.510
0.510
3.150
0.3SO
11 .180
0.610
1 .260
	 27, I5.fi
70.730
0.600
1.020
?.?TO
BLACKHAWK 0.550
MACRRIOE 0.67O
PRASIF. ROSE 0.720
REJT>_fig_CE_ 	 6a.96Q_
ROCK CR5FK 3.120
VIKING 0.490
SILVER 0.240
MEAN
STO OEV
MI NIMUM
MA XI VUM
MEDIAN
12.335
23.357
0.240
70.73.0
0.070
-0.030
0.160
O.I 1 0
	 0.400__
0.600
0.530
-0.060
O.~350~
-0.200
0.670
0.74O
0.200
0.720
0.7*0
	 C.610._
0.820
0.430
0.360
0.416
0.325
-0.200
0*415""
1.478.
0.175
0.279
0.315
_O.Ji4
3.O75
0.152
0.129
_0*1J86_
0.368
0 .048
0.245
_0.251_
0.239
O.202
0.262
	 O m 62. 1 	
0.451
0.135
0.240
0.448
0.688
0.048
3 ,075
0.242
	 0_iQ3.Q
0. 180
0.235
0. ?80

7. 100
-8. 73823B3. OOO
0.348 3.500
1.236 46.600
9.999 ?3.IOO
1.210 5.455
0.072 4.511
0.137 -0.553
_0_tJ54 	 29,*9. 74_
0.239 103.385
0.058 -2.063
0.481 8.460
	 0.465 	 25. I69_
0.192 0.574
0.057 8.532
0.068 7.827
	 Q t 242 1 73 «X&9_
0.001 31.538
0.077 2.735
0.149 0.613
31 .800
24.600
44.600
506. 7 OO
986. 700
49.500
21 .400
_LD7.300_
21 .600
16.900
16.000
_a3a.*aa0_
35.500
7.200
3. OOO
0.185 20.741 258.349
0.257 43.265 575.196
O.C30 -8.738 3.500
1.230 173.7B923a3.JLQQ_
0. 109
4.983
28.200
..0.930
0.170
-1.670
0.35O
0.44A
0.33O
0.350
0.410
	 Q»J2CL-
0.0
0.200
0.62O
	 o..*.5a_
0.700
O.S6O
0. 67O
0. ?6O
0.2*O
0. *6O
-0. IOO
0.272
0.519
-1.670
0.930
0.35O
_LB.J)9»
7.943
1.917
4.660
P. 745
6.150
4.564
	 3.462_
5.1 39
3.960
5.136
_jj..asi 	
0.402
5.093
s.eia
7.47O
5.128
1.986
3.900
6.367
7.688
1.917
1K.094
5.13Q
_1..267 	 5..2J3 	
6.593 61 .446
5.117 -1.142
3.029 5.385
3.534 3.203
5.859
3.998
2.f 9T
__3.0*7_
5 .1 SO
3.168
1 .952
6.51B
1.791
2.154
6.^91
19.956
0.0
3.125
6.79B
7.40B
2.B21 5.360
2.241 4.223
1.920 5.583
5. S2* 30.010
3.897
4*290
3.694
1 .655
1 .267
6.593
3.351
2.186
2.039
-O.O91
9.023
15. 198
-1 .143
61. 4*6
4.718

-------
                            Table C-C  CCont'd}.  Data Used  for Model Testing
to
VO
ID NAME
295 BALDWIN
308 HORSEHOE
310 LOU YAEC.ER
314 SANGCHR1S
316 SLOCUM
321 WEEMATUK
341 SYLVAN
394 BEACH CITY
4"b7 Q5H*UGHNFS~S\
410 SHAWNEE
494 AHQUAB1
_495_niG CREEK 	
406 BLACKHAWK
500 MACBRIOE
501 PRA.RIE POSE
503 f>ED ROCK
504 WtfCK CftEFK
507 VIKING
505 SILVER
Mt AN
STO oev
MINIMUM
MA XI *>UW
MEDIAN
CHLA
U. 300.
.000
1H2.300
10.700
19.300
221 .100
a. ooo
47.5OO
10.870
5.520
39.600
8.60O
16.900
49.700
17.100
17.400
	 J4.70Q 	
18.400
26.0OO
95.300
41.614
58.945
5.520
221 .100
17.250
ZSEC ALPH DONN
0.986 J.345 1.800
0.236- 6.667 6 . 60O
0.437 0.0 B.200
0.264 5.963 3.600
Q.625. 2f078 g. 5.0Q.
O.323 O.O 9.200
O.BS6 1.699 O.SOO
0.767 0.739 O.ZOO
0.279 5.tl5 4.000
0.526 2.992 0.100
0.653 1.355 0.0
O.TflO 1.671 6. BOO
I.5&2 	 P. 556 	 0.2QQ
0.300 4.047 0.0
1.057 1.058 0.0
0.922 1.276 6.400
A »_6 76 __£.».P. 16 	 L.flQO_
0.49S 2.799 6.600
1.041 O.aiA 0.600
0.439 0.919 5.000
0.661 2.191 3.075
0.342 1.950 3.216
0.236 0.0 0.0
1.562 6.667 9.2OO
0.639 1.527 1.400
TPM OPM
0.044 0.007
0.160 0.065
0.127 0.018
O.lfl6 O.O76
o.psp p. oq?
O.B05 0.302
0.069 0.011
0.1 70 0. Ol 7
Q.I 22 O.pl5
0.208 0.159
0.060 C.009
0.062 O.O09
	 P..P»5 	 JB. Oil
0.185 0.020
0.061 0.010
0.056 0.010
~~olo65 otoo7
0.075 O.O17
0.193 0.034
0.147 0.046
0.166 0.072
0.044 0.007
0.805 0.302
O.O9R 0.017
INM
4.600
0.705
1 .600
1.970
0.20O
1.770
0. 1 30
1.99P_
3.070
2. 380
0. 335
	 6.470 	
0. 1 30
2.040
0.210
O."l30
O.S70
1.566
1.656
0.130
6.47O
1 .500

-------
                               Appendix D
                      Water Quality Impact Results:
           Additional Interpretations and Sensitivity Analysis

 Introduction

     In Section  5 of this report,  the application of the watershed and

 impoundment water quality models  is discussed.   The purposes of

 this appendix are  (1) to present the details of the water quality impact

 results;   (2) to present some supplementary interpretations of these

 results;  and (3) to present some preliminary results of a sensitivity

 analysis applied to the watershed/water body model framework  .

Water Quality Impact Results

     The watershed and impoundment models have been applied to assess the

water quality impacts of each of 11 agricultural practices on each of

three field/soil types.-.characteristic of the. Black  Creek Watershed,

Indiana.  For each practice/field/soil type combination, the analytical

framework has been applied to a homogeneous watershed of 200 km  draining
                                               2
into an impoundment with a surface area of 5 km  and a mean depth of 4

meters.   Table JD-1 identifies some of the key variables used  to depict

the water quality impact results.   These results are presented in Tables

D-2, D-3 and D-4 for the lowland,  ridge, and upland soil types, respectively.
                                     292

-------
            TABLE D-l.   DEFINITIONS OF VARIABLES IN TABLE D-2 to D-4
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

16


17


18


19


20
21

22
23
24
25
26
27
Definition
Runoff rate
Gross erosion
Sediment delivered to impoundment
Sediment trapped in impoundment
River nitrogen concentration
River phosphorus concentration
River sediment concentration
River light extinction coefficient
Soluble phosphorus loading
Snowmelt (crop residue) phosphorus loading
Available particulate phosphorus loading
Total phosphorus loading
Impoundment outflow nitrogen concentration
Impoundment outflow phosphorus concentration
Impoundment outflow sediment concentration

Impoundment light extinction due to sediment

b
Impoundment light extinction due to color

b
Impoundment light extinction due to algae

b
Total impoundment light extinction

b
Secchi disc transparency
Annual average impoundment light extinction
coefficient
b
Nitrogen resistance to algal growth
Phosphorus resistance to algal growth
b
Light resistance to algal growth
Total resistance to algal growth
Chlorophyll-a concentration

Units
m/yr
kg/m2-yr
A
kg/m -yr
kg/m2-yr
g/m3
g/m3
kg/m3
m
g/m2-yr
g/m2-yr
g/m2-yr
g/m2-yr
g/m3
g/m3
kg/m3
b

m


m 1

_
m

-l
m

m

m"1
(g-Chl-a/m3)"1
(g-Chl-a/m3)"1
(g-Chl-a/m3)"1
(g-Chl-a/m3)"1
g-Chl-a/m3

b - Summer average values.






                                    293

-------
                                 TABLE D-2.   WATER QUALITY RESPONSE TO PRACTICES FOR LOWLAND SOIL
         PRACTICE:
10
VO
ICE:
able
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
1
. icc-cv
0.173
O.762
4.957
4.763
1 1 .076
O.I 86
0.496
47.477
1 .105
0 .O
0.750
1 .656
5.443
O .093
O.C19
0 .551
0.520
0.663
1.773
O.936
3.252
3.CE6
26.661
15.532
45.279
O .022
22.3C4
2
1CC-CH
0. 178
0 . 245
2.322
2 .228
11. 076
0. 1 96
0 .232
2
-------
                                TABLE D-3.   WATER QUALITY RESPONSE TO PRACTICES FOR RIDGE  SOIL
10
VD
PRACTICE! i
*Variai>l«s2CC-CV
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
0.064
2.049
11 .065
10.729
18.460
0.139
1 .107
95.22?
0.408
0.0
0.969
1 .376
7.060
0 .046
0.034
0.953
0.111
0.41C
1.514
1 .097
3.232
2.272
55.26ft
15.491
73.231
O .014
23.562
2
2CC-CH
O.C42
0.927
5.360
_ 5.191
13.460
0.131
0.536
46.74B
0.403
0 .31*
0.590
1 .306
7.030
O.C63
O.C18
0.510
0.112
0.542
1 .205
1 .378
1.906
2.372
39.643
13.269
55.294
0 .Gl«
24.1 33
3
2CC-NT
0.019
0. 537
3.262
3.145
21 .930
0. 157
0.326
29.035
C. 337
0.623
0.552
1.568
7.663
0.069
C.012
0. 313
0.124
0.706
1.202
1.390
1.411
	 a..i9j._
27.732
12.595
42. 518
0.024
20.051
4
2C3-CV
0. 064
2. 09S
11. 3C7
1 0. 964
1 3. 060
0. 136
1.131
97.175
C.4C1
0.0
0.956
1. 35*
5.956
0.045
O.C24
C.971
0. IOC
0. 398
1.510
1 . 100
3. 254
2.920
57.010
15.536
75.365
0.013
ze.eie
5
2CD-CI-
O.C42
1.171
6.621
6.415
13.06C
C.125
0.663
57.313
0.2*7
C.2CC
1 .246
5. 556
0.055
C.022
0.612
O.OSC
1.222
1.35E
2.143
45.864
12.619
62.323
0.01C_
26. f.f c
6
2C3-NT
C. 019
C.876
5.1C3
4. 931
14. 740
C. 139
C. 51C
44.1 as
C.362
C. 360
C.672
1.394
6. 343
C.069
C.017
C.469
Ci 076
C. 577
1.1 ei
1.4C6
2. 648
12.025
52.032
C. C19
25. 1 24
7
2CCWH
O.C22
0.332
2.102
2.C22
6.70J
C.089
0.21 C
1R.63S
C.380
C.224
0.299
C.89-3
4.727
0.056
C.003
C.226
0.076
0.495
C.C37
1.982
0.948
3.553
45.G52
12.023
6C.62"
0.016
1 7.120
6 9
2C6*I — NT2CC-CVT
0.013
0.210
1.375
1.321
8.700
O.C84
0. 139
12.323
0.358
0.264
0.223
0.844
4.727
0.056
O.OC5
0.154
0.06C
0.500
0.754
2.2C1
0.682
3.553
44.689
1 1.713
59.959
0.017
1 7.367
C.C64
1 .455
7.623
17.030
C.I 18
c.soe
70.C09
C.C
0.754
1 .175
6.623
C.047
C.026
0.725
0.123
0.423
1 .312
1 .266
2.5d5
2.461
54.C37
14.321
7C.870
0.014
2J.322
10
2CC-CHT
O.C42
c .6se
3.927
3.769
1 7.060
C .121
C.393
34.7C5
C .416
C.332
0.463
1 .2 10
6 .623
C.065
0 .014
C .J9C
C.126
0.559
1 .116
1 .466
1 .589
2.461
36.350
12.630
53.641
O.C19
£C .652
11
2CB-NTT
C.019
0.623
3.739
3.607
14.040
0. 132
0.374
32.759
0.363
0.428
J .326
1.323
6.186
0.073
0.013
0.374
0.092
0.603
1 .109
1 .496
1 .439
2.715
34.41i!
12.632
*9.759
0.020
21.534
                                      for Variable Definitions.

-------
                                TABLE D-4.  HATER QUALITY RESPONSE TO PRACTICES  FOR UPLAND  SOIL
10
PRACTICE:
•Variable
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
1
3CC-CV
C.127
S.9S3
33.927
12.560
O.995
3.393
290.040
0 .325
O.O
0.625
C.950
S.833
O .013
0.115
3.252
0.15?
3.556
0.467
10.274
2.679
233.870
50.000
236.749
0.00?
12.935
2
3CC-CH
0.105
15.567
15.315
12.560
C.C9S
1 .587
136.599
0.363
0.256
0.375
0.993
3
3CC-NT
C.033
1.559
9.430
9.091
15.600
0. 147
4
3CB-CV
0.127
6.095
34.7C1
33.529
9.472
O.oee
0.9*3 3.470
S2.551 296. SOP
C.516
C.4<54
0.472
1.472
5.833 6.526
0,026 0.054
0 .055
1.565
0. 166
	 Q_.2_37_
2.009
0.827
5.233
2.679
102.967
20.730
126.577
O.OOB

0.034
0.959
0.241
0.459
] .699
0.977
3.639
2.573
46.432
16.331
65.335
0.015
6. 355
0. 307
0.575
4.974
0.0 12
0. 1 17
3.324
0. 148
0. 094
3.606
0.46C
10. 4£fi
3.377
264.661
50.000
318. C37
13.45?
5
3CB-0
C.1C5
3.4C2
19.62?
19.155
9.472
C.089
l^C.137
C.332
C.162
0. 392
c.aee
4.974
0.02C
c. cee
1 .937
0.144
0. 1 75
2.29C
0.722
6.263
3.377
1 42.02C
25.2C1
171 .596
0.006
13.712
6
3C3-NT
C.C63
2.5 = 1
1 5. C69
14.543
1C.46C
0. 1 2S
1.507
125.991
C.427
C.32C
C.542
1.29C
5.275
C. C35
C. C53
1.491
C.i£3
C.TC9
2.022
C.621
5. 061
3.1 84
74.052
2C.144
57.360
C.01C
1 c.15?
7
C.C76
C.964
£.933
5.761
6.512
C.071
0.593
52.21 1
0.366
0.185
0. 162
C.712
3.935
	 Q..033 	
O.022
C.628
0. 129
0.307
1. 104
1.503
2.31 1
4.269
79.441
1 3.877
97.537
C.C10
S.552
e 9 ic
3CCWI — M3CC-CVT 3CC-CHT
C.C72
_c_._6_fiS_
3.835
2.736
6.832
O.O77
0.389
34.620
0.409
C.22C
J.137
0.766
4.061
0.042
3.015
0.421
0. l£3
0.379
C.993
1.672
1 .762
4.137
61 .934
1 3.066
79.137
0.013
3.823
C.127
0.227
24.426
23.591
1 1 .456
0.078
2.443
209.338
0.326
O.C
C .462
0.778
5.547
0.014
C.034
2.366
C.164
0.123
2.693
C.616
7.630
3.C26
2C6.485
34.843
244.356
C.CC4

C.JCE
1.912
11 .447
1 1 .C41
1 1 .456
o.crsi
1 .145
99. 1O3
0.264
C ,c7C
o.a?2
0 .907
5.547
C.C3C
C .041
i . iec
C.173
0 .275
1 .C13
4 .008
3.C26
ee./36
17.173
1C8.937
C.CC9
10.1 £6
11
3CU-NTT
O.C83
1.611
10.973
10.487
9.936
0.116
I . 087
94.442
0.429
C.340
C.395
1 .164
5.115
0.040
0.039
1 .095
0.194
0.351
1 .690
0.988
3.903
3.283
65.279
16.939
85.501
0.012
8.552
                      *  See Table D-l for Variable Definitions.

-------
Additional Interpretations






     In Section  2 the primary implications of the results are discussed.




Of particular interest is the apparent attenuation of the effects of




erosion control on water quality, as the analysis moves downstream from




the river into the impoundment and when components other than sediment




are considered.  A possible conflict between the water quality manage-




ment goals of controlling both sedimentation and eutrophication using




these types of practices has also been discussed in Section 2.  Additional




interpretations of the impacts of the various practices and soil types




on eutrophication can be derived from  Figs.  D_i^ D_2r and D-3.






     In  Fig.  D-l, the three components of phosphorus loading  (available




particulate, soluble, and crop residue) are depicted for each soil type




and practice.  The importance of residue phosphorus leached by snowmelt




is apparent in the practices involving reduced tillage, despite the fact




that leaching of  only 1 percent of the available residue phosphorus has




been assumed.  As noted in Appendix    B, laboratory studies suggest that




one freezing-thawing-leaching cycle  could release from 5  to  28 percent




of the phosphorus in various crop residues.  The importance of the soluble




phosphorus component is apparent in  the relatively flat, phosphorus-rich,




lowland  soils.   In general,  impacts  of the various practices  on avail-




able phosphorus  loadings  are considerably different  (in magnitude




and often in  sign) from the  impacts  on soil  loss.






     The components of the mean  summer light extinction coefficients  in




the  impoundment  are displayed  for the different practices and soil types




in Fig. D-2.  Extinction  coefficients are  inversely proportional to Secchi




                                   297

-------
 r  o
M

 I  3
 o»
O
z  2
Q
     1
to
CC
O
I
Q.  0
CO  w
O
UJ
_l
CD
<


I
                                     LOWLAND SOIL
                                                  Component
                                     RIDGE SOIL
                                     UPLAND  SOIL
    1 -
 Practice:  1   2   3   4   5  V6   7   8   9  10  11
                                                              75
                                                              50
                                                                CM

                                                              25
   UJ
   i
O  °°
u  QC
   UJ

75 b
25 x
   UJ

   CO


0  §


75 i£
   O

   a.
                                                              50
   UJ
   _J
   CD
                                                              »i
     Figure D-d .
                Coaponents of Available Phosphorus Loading for Different

                Soil Types and Practices
                               298

-------
 300
 200
(~  100
10
 £
 o
 c.
 o

 2  300
o
z
&
(O

UJ
a:
 200
 100
    100
Pract
                              LOWLAND  SOIL
                              RIDGE  SOIL
           ^Nitrogen
           ,Phosphorus
           rLight
                                                           .003
                                                           .005
                                                           .01

                                                           .02
                                                           .04

                                                           .003
                                                            .005
                                                           .01

                                                           .02
                                                           .04

                                                           .003
                                                             o
                                                        .005
                                                        .01

                                                        .02
                                                        .04
 Figure D-3.  Components of Algal Growth Resistance for
              Different Soil Types and Practices
                              299

-------
disc transparencies, which are noted on the right-hand scales of Fig. D-2.




Dissolved color and algae are primarily responsible for light extinction




in the case of the flat, poorly-drained, lowland soils, which are also




relatively high in phosphorus and organic matter.  For the relatively




erodible and phosphorus-deficient upland soils, turbidity (attributed




to non-algal suspended solids) is primarily responsible for light




extinction.  Erosion controls cause substantial  (up to 4-fold) increases




in water transparency only in the upland soil case.  In the other cases,




algal growth and color tend to reduce the relative impacts of erosion




controls on transparency.






    Fig. D-2  depicts the limiting effects of light, phosphorus, and




nitrogen on impoundment algal growth for each soil type and practice.




According to the model used to predict chlorophyll-a concentrations, the




total resistance to algal growth is computed as the sum of the resistances




attributed to light, phosphorus, and nitrogen.  The inverse of this sum is




a measure of the potential chlorophyll-a concentration, as depicted on




the right-hand scales of Fig. D-3.  In general, phosphorus is the most




important controlling factor in all cases examined, while nitrogen is




generally insignificant.  The relatively high degree of phosphorus re-




sistance in the upland soil cases reflect the effects of  (1) the low




phosphorus contents of those soils and  (2) their relatively high erosion




rates, which tend to increase the phosphorus trapping efficiency of the




impoundment because of the influence of sedimentation on phosphorus




settling velocity (see Appendix  C ).  In the upland soils, erosion




controls generally cause less  resistance to downstream algal growth both
                                    300

-------
with  regard  to phosphorus and  to  light.   In  the  cases of  lowland and




ridge soils,, however, chlorbphyll-a levels are not influenced




substantially by  the  practices examined.






     These results indicate the relative impacts of these agricultural




practices on impoundment eutrophication are small, except in the




extreme upland soil case, in which a 10-fold decrease in soil loss results




in a 4-fold increase in algal biomass  (comparing practices  CB-CV and CBWH-NT)




These conclusions primarily result from the following factors:




      (1)   a generally small fraction  (5 to 10) of the particulate phos-




           phorus in soils is biologically available (acid extractable);




      (2)   reduced tillage alternatives create a potential for leaching




           of phosphorus from crop residues during snowmelt periods and




           cause enrichment in surface soil phosphorus levels;




      (3)   the phosphorus trapping efficiency of an impoundment appears




           to be a strong positive function of sedimentation rate;  and




      (4)   algal growth is sensitive to available light and is therefore




           stimulated by reductions in ambient turbidity levels.




An improved picture of the effects of erosion controls and other agri-




cultural practices on impoundment eutrophication could be derived by




obtaining more accurate, quantitative definitions of the above relation-




ships through additional data  compilation and analysis.  Interpretation




of the water quality effects of eutrophication could be enhanced by




expanding the impoundment model to permit direct estimation of dissolved




oxygen levels, as influenced by external  and internal sources of oxygen




demand.
                                   301

-------
                                                                                 D-8
123456789   10    11
     0
Practice:
                                                                                           T!

                                                                                           fl)

                                                                                           w
                                                                                           (U
                                                                                           4-1

                                                                                           0)
                                                                                           H
                                                                                           0)
                                                                                           M-l
                                                                                           
                                                                                           0)  O
                                                                                           c -H
                                                                                           8
                                                                                           a
                                                                                          A

                                                                                          
-------
Sensitivity analysis






     One of the advantages of utilizing a framework of relatively simple




models for evaluating water quality impacts is that it facilitates sen-




sitivity and error analyses.  These help to identify key structural or




parametric assumptions, as well as guide further model development




by indicating the most fruitful areas for investment of additional data




collection and analytical resources.  For a given total investment, the




"most fruitful" area for further work would be that which results in the




greatest degree of improvement in the accuracy of the model or model




framework.  Specific strategies for implementing sensitivity and error




analyses have been discussed in detail by Thomas (1965) and Walker  (1977) .




As model complexity increases, the size, expense of implementation, and




increasing effects of data errors tend to reduce both the feasibility




and the benefits of performing these types of analyses.






     Relatively crude, initial applications of these methods to the water




quality model framework developed and applied in this project are des-




cribed below.  They demonstrate the feasibility and benefits of conducting




sensitivity and error analyses within our model framework.  This means




that they indicate those components within the model framework which are




most important to evaluating both the absolute and the relative impacts




of these agricultural practices on water quality.






     At a basic level, a marginal sensitivity analysis would involve




evaluating and ranking the  first partial derivatives of the predicted




variables with respect to the parameter estimates:
                                    303

-------
     S±.k   =  9k.  ^L.  =   6 in YJJ                                 (1)

       '       Yij S6k       6 in 6kJ




where,     sijk  =  sensitivity coefficient for predicted variable i,

                    case j, and parameter k



           6     =  nominal value of parameter k
            Jc

           y. ,   =  nominal value of predicted variable i for case j



Defined in this way, a sensitivity coefficient equals the percent increase



in the predicted variable resulting from a 1 percent increase in a given


parameter value.  While these derivatives can be evaluated analytically



for simple models, finite-difference methods are usually easier to implement



if the model is computerized.   For a given case (e.g., soil type/



agricultural practice combination) and variable, the parameters can be



ranked according to decreasing absolute values of the sensitivity co-


efficients.  This provides a preliminary indication of which parameters



or processes are most important in determining the prediction.




     This strategy has been implemented for a total of 12 predicted



variables, 33 cases (3 soil types x 11 practices), and 38 parameters.


The parameters, which characterize the various processes represented in



the watershed and impoundment models, are listed in Table D-5 along with



their nominal values and equation references.  To illustrate the method-



ology, results are presented and discussed below for 2 predicted



variables and 9 cases (3 soil types x 3 practices).




     The ranked sensitivity coefficients for the five most critical



parameters in each case are presented in Tables D-6 and D"-7 for predictions



of impoundment light extinction coefficients and chlorophyll-a levels,
                                  304

-------
    TABLE D-5.   PARAMETERS INCLUDED IN SENSITIVITY ANALYSIS

Watershed Model (Appendix B)
Symbol Value Equation
R 160
Ki .50
K2 20.
K3 2.
dCL 1"6?
dgj 1.00
dSA -33
Ktt .34
K5 .20
q .25
q° .178
^ .064
.127 *
Ke 2.0
C .03
D
Y 1.0 *
P 1.0
.50
Ky .01
., .
.6
Ke .5
Yc 10.


(1)
(3)
(4)
(6)
(7)
(8)
(9)
(10)
(11)
(13)
(14)

(17)
(23)
(24)
(26)
(29)

(31)
(33)


Impoundment Model (Appendix C)
Symbol Value Equation
K clay 50
s
silt 120
sand 8000
ao -377
ai -.779
a2 .222
as 0.0
ai+ 1.201
c .223
o
ci -.445
c2 .351
c3 .862
c^ 0.0
e .04
w
ks .085
kB 30.
KG 6.0
PCS 3 . 0
KL -44
fL 1.363
fp 1 . 866
f 1.866
n
(3)


(11)
(11)
(11)
(11)
(11)
(23)
(23)
(23)
(23)
(23)
(27)
(28)
(29)
(33)
(39)
(40)
(51)
(65)
(65)
(65)

Parameter values for lowland, ridge, and upland soils, respectively.
                                305

-------
              TABLE  D-6.   EXTINCTION COEFFICIENT  SENSITIVITIES*
Soil Type Rank
Lowland 1
2
3
4
5
Ridge 1
2
3
4
5
Upland 1
2
3
4
5

(1 CC-CV)
Param. Sens.
Fcs -55
q° -37
K5 -.31
K4 .29
k .28
S
F -.67
cs
K5 -.66
\ -63
ks '61
ait -.46
K5 -.95
Fcs -95
k .91
s
Kit -91
R .84
Practice
(5 CB-CH)
Param. Sens.
F -.49
cs
q°
Yc -'31
kB '28
fp -.24
F -.55
cs
K5 -.53
K4 -50
k .48
S
ait -.46
F -.88
cs
K5 -.85
ks .83
Kit -80
R .73

(7
Param.
o
qr
F
cs
kB
Yc
q
kB
p
F
cs
KS
ait
F
cs
k
s
K5
Kit
R

CBWM)
Sens.
.51
-.42
.36
-.35
-.31
.47
-.44
-.35
-.35
-.33
-.67
.57
-.54
.51
.43
*  A sensitivity coefficient represents the percent increase in the
   predicted value resulting from a 1 percent increase in the res-
   pective parameter.
                                  306

-------
respectively.   For the extinction coefficients, Tables D-6 indicates  the

importance of the assumed ratio of summer-average to mean-annual



               TABLE D-7.  CHLOROPHYLL-A SENSITIVITIES *

Practice
Soil Type Rank
Lowland 1
2
3
4
5
Ridge 1
2
3
4
5
Upland 1
2
3
4
5
1 (CC-CV)
Param. Sens.
ait —.75
f -.59
P
ai .54
*£ *48
fL -.34
ait -1.68
ai .96
f -.75
P
a -.53
o
KL '30
ait -3.83
ai 1.61
a -.89
o
f -.81
P
K5 .32
5 (CB-CH )
Param. Sens.
f -.56
P
ait --50
K .46
L
ax .42
£L
a4 -1.17
ai .77
f -.73
P
a -.43
o
K .27
ait -2.81
ai 1.36
f -.83
P
a -.75
o
KS • 56
7 (CBWM)
Param. Sens.
f -.58
P
Yp -39
K. .37
L
q -.37
a .32
1
f -.74
P
a^ =.57
a! .52
q -.31
a -.29
o
ait -1.25
Si\ . 86
f -.81
P
a -.48
o
CD .26
  *   A sensitivity coefficient represents the percent increase in the
      predicted value resulting from a 1 percent increase in the res-
      pective parameter.
                                    307

-------
suspended solids and color concentrations (F  ) , delivery ratio parameters
                                            cs



 (K  and K ) and the slope of the extinction coefficient versus suspended




solids concentration (kc) .  Sensitivity rankings vary somewhat with soil
                       O



type and practice.  For example q° and y~ appear to be important .only in
                                 K      c



the lowland soil, which has a relatively high color contribution.  The




chlorophyll-a sensitivity rankings suggestion the importance of the




phosphorus trapping parameters  (a., a , a )  and the parameters of the biomass




model (f , K , f ).  The listing of only five parameter sensitivity co-
        P   L   L


efficients for each case does not imply that the remaining should be




ignored, but serves here as an illustration.






     A modification of the above procedure has been implemented by




estimating the sensitivities of the relative magnitudes of the predicted




variables to the assumed parameter values.  Relative sensitivity




coefficients are of the form :
               Yij      89k         Yio 69k





            =  smtY.-A^)



               6 In  9



The relative magnitude of any predicted variable is defined as Y../Yj_»



ratio of the value for a given case to the value for an assumed base case



A sensitivity coefficient evaluated as prescribed above represents the



percent increase in that ratio resulting from a 1 percent increase in a



given parameter value.  When the model framework is being used to



compare practices, these relative sensitivity coefficients are perhaps





                                    308

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more important to consider than are the absolute versions.  Parameters

have been ranked according to this scheme using practice 1 (continuous

corn with conventional tillage) as a base case for each soil type.


     Results are presented in  Tables D-8 and D-9 for predictions  of extinction

coefficients and chlorophyll-a levels, respectively.  In comparing these



 TART.K D-8.  EXTINCTION COEFFICIENT SENSITIVITY* REIATIVE TO PRACTICE 1

Soil Type Rank

Lowland 1
2

3

4
5
Ridge 1
2

3

4

5
Upland 1
2

3

4
5

Practice
5
Parameter
K5
K
it
k
s
R
dCL
K
5
k
S
F
CS
K
4
R
R
K
S
K
i*
a
k
S
(CB-CH)
Sensitivity
.11
-.10

-.10

-.10
-.09
.13
-.13

.13

-.12

-.12
-.11
.11

-.10

-.10
-.08

7
Parameter
K5
K
**
k
s
R
dCL
ks
F
CS
K
5
K
1*
kB
K
5
R

K
"*
ks
F
CS
(CBWM)
Sensitivity
.22
-.21

-.21

-.18
-.18
-.35
.33

.31

-.30

.26
.41
-.41

-.39

-.35
.28

*  A sensitivity coefficient represents the percent increase in the predicted
   value resulting from a 1% increase in the respective parameter.
                                      309

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 results with  those  in  Tables D-6 and D-7,  two general observations can be made

 First, the  lists  of most  important parameters change  somewhat  as  the

 ranking criteria  switches from absolute  to relative sensitivities.

 Secondly, the relative  sensitivity coefficients  are generally  lower in

 scale.  This  essentially  reflects that the model framework  is  more
         TABLE D-9.  CHLOROPHYLL-A SENSITIVITY* RELATIVE TO PRACTICE 1

Soil Type Rank

Lowland 1

2

3

4
5
Ridge 1
2

3

4

5

Upland 1
2
3

4

5
Practice
5
Parameter
a
if
a
1
a
o
K7
FD
a4
a,
1
K_
7
a
o
d
SI
a4
KL
a.
1
K
5
K
4
(CB-CH)
Sensitivity
.25

-.12

.07

.06
-.04
.52
-.19

.12

.10

.07

1.02
.33
-.25

.24

-.23
7 (CBWM)
Parameter Sensitivity
a .46
"*
a1 -.22
1
a .12
o
K -.11
L
q -.11
a4 1'12
an -.44
1
a .24
o
^ .20

d .16
SI
a4 2.58
a -.76
a .42
0
K .23
7
K. .18
L
*  A sensitivity coefficient represents the percent increase in the
   value resulting from a 1% increase in the respective parameter.
predicted
                                     310

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accurate for estimating the relative impacts of the various practices




than for estimating the absolute impacts.






     For estimating extinction coefficients in a relative sense, the




most important parameters appear to be those related to sediment delivery




(Kg,  K ,  dCL)f rainfall erosivity (R) , and suspended solids light




extinction (kg).  Note that FCS is considerably less important here,




than when the parameters are ranked according to absolute sensitivities




(Table  D-6).   This  suggests  that a  given  percent error  in the  estimate of




this parameter would have a nearly constant percentage impact on the




computed values of the light extinction  coefficients for the various




practices.  This impact is subtracted out when relative sensitivities




are considered.  In evaluating relative chlorophyll-a levels (Table  D-9),




the phosphorus trapping parameters appear to be most important, along




with the leached fraction of crop residue phosphorus, K?.




     Based upon  interpretations; of the results of the above sensitivity




analyses,  the most important parameters  and processes  for  estimating the




relative impacts of agricultural practices  according to  various criteria




are  summarized in  Table  D-10,  The  sensitivity rankings are typical  of the




various  soil  types and practices  considered.  They provide tentative




indications of the most  important  areas  for future model  improvement.




At a higher  level  of sophistication,  parameters  could  be ranked based




upon their respective contributions to the  total  variance  of  predictions




derived from the model.   Such  an  error analysis  could  alter,  somewhat,




the rankings  presented in Table D-10, The  merits of such  an  analysis




should be explored in follow-up work.
                                    311

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    TABLE D-10.  SUMMARY OF MOST IMPORTANT MODEL PARAMETERS FOR ESTIMATING
    THE RELATIVE WATER QUALITY IMPACTS OF VARIOUS AGRICULTURAL PRACTICES
Criteria
Parameters
                        Processes
River Sediment Concentration &
Impoundment Sedimentation
 CL'
K
     K
sediment delivery
texture enrichment
River Phosphorus Concentration &
Impoundment Phosphorus
Loading
VK4
R
                     sediment delivery
                     residue leaching
                     rainfall erosivity/
                     gross erosion
Impoundment Nitrogen Concentration  C , C , C.
                                    q
                                    F
                                     D
                     nitrogen trapping
                     total flow
                     denitrification
River Light Extinction Coefficient  d  , d  , K_, K.
                                    R
                     sediment delivery
                     texture enrichment
                     rainfall erosivity/
                     gross erosion
                     solids light extinction
Impoundment Phosphorus
Concentration
Impoundment Sediment
Concentration
Inpoundment Color Concentration

Inpoundment Light
Extinction Coefficient


Chlorophyll-a

V V al
dsr dcL
Ks
*8
I\^ f I\—
V V dCL
*s
Fcs
a4' ao'al
Kr
phosphorus trapping
residue leaching
sediment delivery
sediment trapping
soil organic
natter enrichment
texture enrichment
sediment delivery
solids light extinction
seasonal variations in
color and suspended solids
concentrations
phosphorus trapping
residue leaching
algal growth
                                     312

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REFERENCES, APPENDIX D

Thomas, H.A., Jr. "Operations Research in Disposal of Liquid Radioactive
     Wastes in Streams", Harvard Water Resources Group, Cambridge, MA,
     Dec. 1965.

Walker, W.W., Jr. "Some Analytical Methods Applied to Lake Water Quality
     Problems" Ph.d. Thesis, Engineering, Harvard University, (University
     Microfilms, Ann Arbor, MI), 1977.
                                    313

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                                Appendix E




                     Discussion of Benefit Estimation
     This appendix presents the results of the literature review and work




on benefit estimation.  The discussion follows the outline described in




Table E-l.









Introduction






     We begin by emphasizing several points frequently made.  As is gen-




erally agreed upon among economists willingness-to-pay is the appropriate




measure of benefits.  The choice facing society is not between clear water




and polluted water, for example, but between various levels of pollution.




It is the incremental or marginal values that are important in making




decisions.  The "demand" for water quality (the analog to market demand) is




the aggregate of how much individuals will give up (will pay) to enjoy




additional increments of improved water quality.






     The economic theory for valuing benefits is well developed.  A com-




plete theory on the provision and use of public goods, those which are




enjoyed in common, such as the water quality of a stream, has been developed.




From the literature of welfare economics we get such concepts as the




Pareto Optimum criteria, consumer surplus, the social welfare function,
                                    314

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                              Table E-l
                Outline of Benefit Estimation Discussion
1.  Points from proposal
    A.  Willingness to Pay — Appropriate Measure
    B.  Economic Theory Well Developed
    C.  Not so Easily Applied
        1)  Lack of Market
        2)  Problem of "Intangibles"
        3)  Thorough Analysis Impossible
        4)  Data Needs Immense
        5)  Equity Question
2.  EPA Needs (Our Impression)
    A.  Further Pollution Control Expenditures Assessed
        on Basis of Benefits
    B.  Generally Accepted Methodology
        1.  EIS Review
        2.  Support Regulatory Standards
    C.  Policy Direction
3.  Criteria
    A.  Ease of Application (Data)
    B.  Identified Pollutants
    C.  Theoretical Validity
    D.  Pollutant	-^   Environmental (Water) Quality
        Value Measurement
    E.  Benefit Quantification
    F.  Distribution of Impacts
    G.  Generalizability
4.  Examples
                              315

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and the equi-marginal principle for selecting the appropriate level of




pollution abatement.






     But as is well known, these general principles for management of public




goods are not so easily applied.  The problems of the misallocations of




resources and externalities  are not theoretical but  empirical ones.




For instance, there is the problem of the  lack of a  market.  As we  said,




public goods are enjoyed in  common.  They  are shared,  so  they are not




contained in market transactions  and they  have no market  price to use




to define demand.  The question of intangible benefits is also complex.




A hypothetical demand curve  can be derived from aggregating  individuals'




willingess-to-pay  (for increased  increments of a public good, as mentioned




above).  One approach to estimating willingness-to-pay is to calculate




the damages that would occur if a project  were not undertaken.  However,




this method still underestimates  psychic benefits  (called "intangibles").






   In most cases a complete, thorough analysis is impossible because it




is too difficult to estimate the  multitude of impacts  of,  say, a change




in water quality even though it is said  (by Kneese and others) that a




materials balance concept should  be used.  The existence  of  interactions,




substitutions and indirect benefits in most water quality control prob-




lems contributes to the difficulty of conducting an  adequate analysis as




defined by economic theory.  Furthermore,  data needs are  immense and the




expense and personnel necessary for data collection  are great.  These




are the greatest impediments to good empirical benefit estimation work.




Examples of the types of data used for the various methods of estimating
                                   316

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water quality benefits are survey data, property sales prices, detailed

studies of physical damages, and origin and destination data from travel-

lers.  Many methods use data that must be collected anew for each study.


   In addition to these obstacles there is the equity question.  Environ-

mental control measures are inherently redistributive and there is no

generally accepted method for the resolution of the conflict of interest

among those who gain and those who lose from environmental quality

improvement.  This issue is addressed  in Section 6 of the report.



Need for Benefit Estimation


   Prom discussions with personnel in  EPA  and  review of the  literature

including the study of water reuse and benefit estimation done by ERCO

for  the EPA  (1977).    The need  for benefit estimation  can be summarized

as follows.


   • A time may  come  when the national (or industry-specific)  pollution
     control  effort will  reach  a point at  which further expenditures
     must be  assessed on  the basis of  benefits received.

   • There  is a  need  to develop a generally accepted methodology for
     estimating  project benefits;  something straightforward and  applica-
     ble to multiple  situations including  review of EIS reports.

   • Regulatory  standards may  need to  be supported by benefit estimation.



 Criteria for  Benefit  Estimation Methods.


   Meeting these needs will be a difficult task.  To assist in evaluating

 methods  of benefit estimation  we developed a set of criteria which define

 a "satisfactory" benefit  estimation framework:


                                     317

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    A.  Ease of application  (availability of data).

        Does the methodology rely on data generally available, such as
        the census and property value assessments or must it be collected
        systematically each time?

    B.  Consideration of identified pollutants.

        This criteria is necessary to relate the benefit estimation to
        non-point source pollution control in general and, specifically,
        to apply it to particular management practices.

     C.  Theoretical validity.

        This necessity was  covered earlier  in  our discussion  of willing-
        ness-to-pay.  In practice, it usually  means development of a
        demand  function rather  than estimation of gross benefits  or use
        of a "judgment value" for benefits.

     D.  Investigation of the relationship between pollution levels and
        value measurement.

        The reasoning behind this requirement  is the same as  for  B above,
        to be specific.

    E.  Quantification of benefits.

        To compare with marginal costs we must be able to discuss incre-
        mental benefits.  We must have some measure of benefits to make
        a decision — they  are  not infinite.

    F.  Identification of distribution of impacts.

        This criteria concerns  the equity question.  We must know who
        gains, who loses, and the consequences of alternative controls
        to facilitate a decision.  This is not necessary to insure
        national economic efficiency but it certainly is recognized as
        important.  (See, for example, the hearings on the Principles
        and Standards in response to the President's concern.)

    G.  Generalizability of methodology.

        For it to be useful to meet EPA needs, the technique must not
        be limited to a single problem or region.

Our assessment or benefit methodologies may show that certain techniques

appear more promising than others for specific pollutants or impact

groups or land/water configurations.
                                   318

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Examination of Examples of Benefit Studies


    Having reviewed over thirty recent benefit studies we have selected

eight representative to examine in detail in light of the above set of

criteria.


1.  Dennis P. Tihansky, "Damage Assessment of Household Water Quality,"
    Journal of the Environmental Engineering Division, ASCE, Vol. 100,
    No. EE4, August 1974.


    This paper develops a comprehensive framework for analysis of

national mineralized water supply damages.  The aggregate mineral content

of water, i.e., the total dissolved solids  (TDS), increases the depreci-

ation rate of household items and adds to their maintenance needs.

Tihansky derives functions relating these impacts on households to various

levels of dissolved mineral constitutents in the water supply.  Data from

household surveys are used to derive damage relations comparing the

average service life of twenty household items to TDS.  For example, the

average life span of toilet facilities decreases exponentially as the TDS

content in water supply increases.


    Tihansky defines monetary impacts as the sum of annualized capital

costs plus operation, maintenance and repair  (OMR) expenses.  Total

household damages in monetary terms are calculated from the individual

household item damage equations  (TDS and hardness versus dollars).

Tihansky applies these damage functions to state-by-state household

statistics, such as income levels, and data on water quality from

USGS and municipal water supply surveys.  This yields regional estimates

of damages, expressed as intervals to account for variability among

households and to reflect water quality sampling errors.

                                     319

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    Significant impacts occur in the midwest and southwest.  The

least impact is in the south, northwest and New England.  The mean per

household for the United States is $33.50 per year.

    The  final step of the analysis consists of the  calculation of

the percent of damage caused by man-made as compared to natural  sources

of TDS load.  Tihansky uses a generalized estimate  of approximately

thirty percent, derived from a study of the Colorado River  and another

of a New Jersey river.


    Tihansky"s analysis meets all our criteria.  For data he relies

on existing studies relating TDS to household item  damages  (A).  He

treats a specific pollutant  (B).  He develops functional relationships

between  damages and pollutant  (C).  The relationship between value

measurement and levels of pollution is explicit  (D).  Benefits are

quantified in dollars (E).  The distributional aspects are  addressed

in terms of the differences in impacts among states and regions  in the

United States (F).  His methodology is general enough to be applied

to state and regional data (G).
2.  Sharon Oster, Survey Results on the Benefits of Water Pollution
    Abatement in the Merrimack River Basin, Department of Economics.
    Yale University, September 1976.  Also in Water Resources Research,
    October 1977.
    The report deals with the estimate of benefits of water quality

improvement derived from a frequency of use/willingness-to-pay survey

conducted in 1974 in the Merrimack River Basin.  The study consisted of

a telephone survey of 200 residents of towns along the river.  The

questionnaire requested information on willingness to be taxed or to


                                   320

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pay a yearly charge for the river to be cleaned up.  It also asked for




information on increased use of the river for recreation activities if




it were cleaned up.




     The  results  of the survey  showed  that the average  aggregate willing-




 ness-to-pay for  river clean-up is  slightly over $12.00 per year.  The




 mean increased use of a clean  river is thirteen days per year.   This




 is  a willingness-to-pay measure for a complete river clean-up.






     Oster analyzed the survey  results by cross-tabulating income with




 willingness-to-pay data and with increased use.  She found that both




 increased with increased income.






     Oster's study meets criteria E, F and G.   Benefits are quantified in




 two ways, dollars and recreation activity days (E).  The equity question




 is  explicitly addressed in terms of differences in willingness-to-pay




 of  different income groups (F).  The  method of benefit calculation is




 generalizable, although the data would have to be collected for each




 study area (G).






     Critera A, B, C and D are  not met.  As explained above, a survey  must




 be  conducted each time the methodology is to be applied (A).   Oster does




 not specify pollutants (B), she asks  about payment to  "clean up" the




 river.   This is  ambiguous.  An alternative approach was used by Gramlich




 in  his thesis on the Charles River (Harvard University, March 1975) who




 uses a more theoretical questioning technique, posing  levels of clean




 water corresponding to standards for, for example, "swimmable" quality




 water. Although  she investigates willingness-to-pay, Oster does not
                                    321

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develop a functional relationship between willingness-to-pay and alternative

water quality levels (C).  Oster also does not specify a relationship between

pollution, water quality and personal utility (D); she considers total

utility for a total clean-up (undefined).

3.  J. C. Day and J. R. Gilpin, "The Impact of Man-Made Lakes on Residen-
    tial Property Values:  A Case Study and Methodological Exploration,"
    Water Resources Research, Vol. 10, No. 1, February 1974.


     This study does not concern water quality impacts.  However, it

does explore certain methodologies that may be important for assessing

the benefits of water pollution control.  The analysis uses a market

study method and a survey method to investigate  the benefits of develop-

ment of a reservoir on nearby property values.


     Data are collected for  455 single  family and apartment houses

surrounding the project area.  A regression analysis  is performed to

determine the factors  associated with residential assessed property

values  (sales values would have been more meaningful,  the authors

contend, but only  a small number of records were available).  Distance

from the reservoir predicted only 0.8 percent of  the variation.  Day

and Gilpin feel that this result suggests that the reservoir project

had not  influenced assessed  property values;   so they tried an alter-

native approach, behavior analysis.


     A survey was  conducted  of 35 percent of the dwelling units surround-

ing the project area to determine residents' perceptions of the value

of the reservoir.  Ninety-four percent  did not know about the project

when they moved to the area.  The questionnaire  requested interviewees
                                     322

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to rank the factors which contributed to the benefit of living in the




study area.  Only two percent ranked the reservoir  in  their top four




choices and these people lived adjacent to the project area.  Seventy-




one percent of those interviewed felt that the reservoir project did




not affect property values.  Day and Gilpin conclude that benefits are




restricted to a small area contiguous to the lake property.




      Since this  study uses two  methodologies,  they will  each be assessed




 in light of our  criteria.   The  market study meets criteria A,  C, E




 and G.   The survey methods meets only F and G.  The market study approach




 is appealing because it uses generally available data,  land value assess-




 ments (A).  The  survey  method,  as in the Oster study, has to be repeated




 each time it is  used.   Regression analysis is a theoretically valid ap-




 proach (C).  The behavior analysis methodology is qualitative and there-




 fore not theoretically  valid.  It could, however, be a helpful complement




 to a more rigorous method.  The market study quantifies benefits (E).




 The survey does  not, although benefits of the reservoir are compared




 to other benefits through ranking.  The property value study does not




 address the equity question although it could be used to do so.  The




 questionnaire, however, does show that certain benefits accrue only to




 those living adjacent to the water body  (F).  The property value method-




 ology is generalizable  (G).  So are the survey and ranking analysis




 methodologies but they must be repeated each  time.






      Neither methodology meets criteria B or  D since the  study  was




 not concerned with water quality, although they  could be  adapted to




 study water quality impacts.  In particular,  the behavior analysis







                                     323

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methodology might be used to investigate the relationship between

water quality and a value measurement.

4.  Dow Chemical Company,  An Economic Analysis of Erosion and Sediment
    Control Methods for Watersheds Undergoing Urbanization, Final Report,
    Midland, Michigan, February 1972.


    This is one of the few analyses specifically concerned with sediment

as a water quality determinant.  The  study relies on available cost data

relating to sediment damages and presents average damage costs per ton

of sediment entering the stream system.  It is part of a larger report

focusing on soil losses from urban construction sites which analyzes

the cost and effectiveness of numerous sediment control systems.  The

economic impact of sediment in water  was estimated for the Potomac River

below the confluence with Seneca Creek.


    The study assumes that a reduction of a unit of sediment provides

a proportional reduction of cost, an  assumption which probably holds for

large scale sediment removal but does not apply to small reductions.

From measurement of the existing total sediment transport in the river,

a reduction in yearly average sediment load was estimated for the river

to be considered "clear."  This amount was used to reduce annual dollar

damage estimates to dollars per ton of sediment removed.


    Damages per ton of sediment to downstream water bodies are calculated

in terms of uses which are defined as:  metropolitan water supply;

industry including electric power, dredging and commercial fishing;

recreation including fishing and boating; aesthetics; and flood damage

abatement benefits due to sediment control impoundments.  Calculation

methods are as follows:

                                   324

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Metropolitan Water Supply






    The difference is calculated between chemical treatment costs,




assuming the water is clear and existing treatment costs.  Costs are




linear versus sediment concentration so cost differences are divided




by required reduction in sediment per year to give cost per ton of




sediment removed.








Electric Power  Improved cooling condenser design prevents plugging




from fine particles so cost is not reduced by lower sediment concentrations.








Dredging  From available data a cost per cubic yard for dredging is




developed which includes disposal costs.  This is multiplied by the




past average amount dredged and divided by the required reduction in




sediment per year.








Commercial Fishing  The present dockside value of fish and shellfish




is calculated.  From data  on the impact of suspended  solids on trout




and shellfish density as a percent of  normal  for "clean"  streams,




the increase of commercial catches is  calculated assuming that it




would  increase proportionately  to the  fish population.   The increase




per ton of sediment  is then determined.








Recreational Fishing  An average number of fishing  days  is  estimated




from Fish and Wildlife  Service  forecasts  and an average value per
                                     325

-------
man-day for fishing is assumed.  The average annual value of all




fishing days in the area is calculated and the increase in value is




calculated assuming the same fish density increases with reduction




in sediment as for commercial fishing.  Value returned per ton




of sediment is determined.






Boating  The number of pleasure boats using the tidal Potomac is esti-




mated and annual total recreation expenses are calculated on the




basis of amortization of an assumed average original cost and annual




expenditures per boat.  A percentage increase in boating due to clean




water is assumed and a percent contribution to this amount due to




sediment removal as well.  The potential increase in value is calcu-




lated and divided by the annual tons of sediment required to be removed.






Aesthetics  The number of visitors to the area is estimated and a




proportion who are tourists is assumed.  As a matter of national




pride to help reduce sediment in the Potomac, an amount per visitor




($.25 - $.50) is assumed as reasonable value to ascribe to



aesthetics.  Based on this assumed value, the average amount of




damage per ton of sediment removed is calculated.






Flood Relief Incidental to Sediment Control  The annualized flood




damages in the Potomac flood plain are estimated.  The number of




impoundments necessary for sediment control is determined and their




flood prevention value in proportion to drainage area retained is




calculated.  This is divided by the annual amount of sediment trapped




to yield a value per ton of sediment retained by impoundments.
                                   326

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     The damages to the users of the Potomac River below Seneca Creek




are summarized as follows in dollars per ton of sediment:




     Metropolitan Water Supply                                   .31




     Electric Power                                             0.00




     Dredging                                                    .67




     Commercial Fishing                                         1.27




     Recreational Fishing                                        .88




     Boating                                                     .84




     Aesthetics                                                 2.56




     Subtotal                                                   6.53




     Flood Relief Incidental to Sediment Control                 .27




       TOTAL                                                    6.80






     The Dow Chemical Company study meets criteria A, B, E, F, and G.




Existing data sources are used for calculating all damage estimates  (A).




A specific pollutant, sediment, is addressed  (B).  Benefits are quanti-




fied in dollars  (E).  The distributional aspects of sediment control




are addressed in the identification of user groups who derive different




amounts of benefit  from  sediment removal  (F).  The methodology is of a




generalizable type  which could be applied to  other watersheds if compar-




able data were available (G).






     Criteria C and D are not met.  Despite the development of what




appear to be functions relating tons of sediment removed to benefits,




they are actually based  on aggregate values and only  assumed to be  linear




 (C).  Judgment values and assumed values and  proportions are also used
                                   327

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in several of the user benefit calculations.  The value measurement for

sediment removal is assumed to be equal to the dollar value of damages

caused by the sediment (D).  This is a valid concept.  However, particu-

larly for recreational fishing, boating and aesthetics, the dollar

values chosen are not necessarily reflective of the benefits derived

from the experience.  Other problems with the analysis include the

neglect of possible higher equipment costs for electric power plants

and the cumulative impact of sediment on flooding.
5.  Alan Randall, Berry C. Ives and Clyde Eastman, Benefits of Abating
    Aesthetic Environmental Damage, New Mexico University Agricultural
    Experiment Station Bulletin 618, Las Cruces, New Mexico, May 1974.
    Randall et al  evaluate the economic benefits to abating the

aesthetic environmental damage associated with the electric power indus-

try as perceived by users of the affected environment around the Four

Corners Power Plant, Fruitland, New Mexico.  The study uses the theo-

retical concept of aggregate bids or benefits for the provision of a

public good as a basis for the analysis.  Efficiency in the provision

of a public good can be achieved by equating the marginal bid with the

marginal cost.


    The bidding game technique of data collection was adapted for use

in this study.  The purpose of the games is to pose hypothetical questions

to measure the willingness of a sample of respondents to pay for envir-

onmental improvements.  Five bidding games were developed to provide

several benefit estimates.  Respondents were shown three sets of photo-

graphs depicting three levels of environmental damage around the power


                                    328

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plant.  The highest level of environmental damage was chosen as the starting




point and respondents were asked to respond yes or no to dollar amounts




to elicit the highest amount they would be willing to pay to improve




the environment to an intermediate level of damage or to minimal damage.




The following types of games were used: regional sales tax (air quality




region); additional charge to electricity bill to all who use the elec-




tricity produced by the plant even if they do not live in the region;




monthly payment (no particular payment vehicle); addition to user fee




for recreationists; compensation game which assumes that the respondent




owns the environment and accepts monthly rent from the industry to




damage the environment.




      Determination of three points on the aggregate bid curve cor-




responding to the levels of environmental damage illustrated were




calculated by aggregation methods appropriate to the stratified random




sampling technique used.  Marginal aggregate bid curves or price curves




were generated by taking the first derivatives of the aggregate bid




curves.  Benefits of an intermediate level of aesthetic damage abate-




ment were estimated at $11 to $15 million annually, while benefits of




complete abatement were $19 to $25 million per year.






      Calculation of the"income elasticity of bid" and the "electric




bill elasticity of bid" indicated that bids for abatement




were higher for households with higher incomes and for households con-




suming more electricity.






      Questionnaire results suggested that financial arrangements




for abatement of aesthetic environmental damage from the power plant







                                     329

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should place the burden on industry and consumers of electricity.

    Criteria C,  D, E,  F and G are  met  by  the  Randall  study.   The

 object  of  the bidding  games are  to produce willingness-to-pay measures

 in response to  changes in environmental damage  (C).   Water quality  is not

 considered in this study but the relationship between aesthetic environmental

 quality and a value measurement  is specifically  addressed in  the bidding

 games  (D) .  Benefits are quantified  in dollars  (E).   The distribution of

 benefits is considered through sampling different  groups including  recre-

 ationists  and by investigating the elasticities  of income and electric

 bill  (F).  Also,  the method of using alternative games elicited infor-

 mation  about the preferences for distribution of the  financial burden for

 abatement  of pollution from the  power  plant.  The  data collection and

 analysis methods were  successfully used in this  instance and  could  be

 applied elsewhere, however, a new  survey  would have to be taken  (G).


    The Randall study  does not meet  criteria  A or  B.   To use  the

 methodology tested in  this study requires the development and execution

 of a reliable survey  (A).  A specific  benefit, aesthetics, is addressed

 in this study,  but the pollutants  are  many, including particulate

 emissions, power lines and strip mining  (B).
6.  Thomas D. Crocker, Robert  L.  Horst,  Jr.  and William Schulze,  Multi-
    disciplinary Research  in Environmental Economics;   Two  Examples,
    paper prepared for the workshop on Multidisciplinary Research Related
    to the Atmospheric Sciences,  National Center  for Atmospheric  Research,
    Boulder, Colorado, August  1977.
    Crocker, Horst and Schulze discuss the valuation of  atmospheric

visibility to illustrate the application of an  economic  value measurement
                                    330

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to a phenomenon generally considered to be intangible.  The area chosen

for study is the Four Corners regions around Farmington, New Mexico

where the unique nature of the extended atmospheric visibility is valued

as a public good.


     The research approach chosen for the study was outlined as follows:

emissionsj-—^ambient concentrations 	^scientific measurement of
                                        visibility reduction
                                                  i
                                        public's perception of
                 value method  ^	   visibility change
     No complete dispersion model was available to establish the first

linkage between emissions and ambient concentrations.  The second linkage

was formed by taking pairs of black and white color photographs of

identical scenes at the same time.  The meteorological range represented

by the black and white photographs was derived from a companion study.

The third linkage was assumed to be one-to-one based on other research.

A survey was used to make the fourth linkage.


     A sample of the population of Farmington, New Mexico was surveyed

and asked to choose which among three color photographs most accurately

represented the ambient conditions during a week in the summer.  The

respondent was then questioned on how he spent his leisure time during

that week including both activities and expenses related to those acti-

vities.  He was then asked regarding thee chosen activities, how he

would change his use of leisure time if conditions were as they appeared

in the other two photographs.
                                    331

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     The authors used the household production theory  (product substi-




tution and unit prices per hour) approach to develop compensated de-




mand functions for visibility from the survey data on time budgets




and expenditures.  Compensating income surplus for a reduction in




visibility was calculated to be about forty dollars a week  (in 1976




dollars).  This is a measure of what the individual would have to be




paid to  tolerate reduced visibility.






      The Crocker study meets criteria C, D, E and G.  A demand function




for visibility is generated using the economic model developed in the




study (C).  Although the study concerns air quality rather than water




quality, the relationship between personal utility and pollution levels




is specified in the research approach and the economic model  (D).




Benefits are quantified in dollars  (E).  The study demonstrates that an




analytically sound implementable model can be constructed to value




aesthetic phenomena (G).  However, the data necessary to implement




the model must be acquired empirically.






      Criteria A, B, and F are not met.  As just mentionned, the data




on which this method is based must be collected for each case to which




the model is applied (A).  The research outline  for the study indicates




that the relationship between emissions  (specific pollutants) and ambient




concentrations  was not specified because of the lack of a complete model




(B).  If this type of model were available for use with the economic




model, then this criteria would be satisfied.  The study does not address




the question of distributional impacts (F).
                                     332

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7.  S. D.  Reiling, K. C. Gibbs and H. H. Stoevener, Economic Benefits
    from An Improvement in Water Quality, prepared for the Office of
    Research and Monitoring, U.S. Environmental Protection Agency,
    Washington,  D.C., January 1973.
    Reiling, Gibbs and Stoevener test a methodology for estimating the
economic benefits accruing to society as a result of water quality
improvements and associated recreation increase at Klamath Lake, Oregon.
Benefits to the local economy are also estimated.

    The demand model is based on two prices which determine the number
of visitor-days which recreationists consume, the cost of travel to the
site which does not vary with the length of stay and the on-site cost.
The methodology designates a critical level of these costs beyond which
the recreationist will choose not to recreate at the site at all.  Cost
variables are expressed on an individual basis rather than for the recrea-
tion group.  Travel costs include transportation, food expenditures, lodging,
camping fees and other expenses.  On-site costs include lodging, camping
fees, equipment rentals, meals and miscellaneous expenses.  Other
variables for the model are demographic characteristics of the recreationist,
income after taxes and site characteristics which include the size of the
lake and use-intensities for water-related activities.  These last are
subjective variables reflecting low, medium and high use for fishing,
boating, etc.  It is assumed that the level of these activities is depen-
dent on the water quality and other physical features of the lakes.  It
is noted that it would be more satisfactory to specify the model with
respect to the biological and physical parameters of the lake directly;
but these data were not available.

                                     333

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    Survey data collected at Klamath lake and at three other nearby




lakes with varied characteristics are used to estimate equations of the




statistical demand model.  Four relationships are estimated:  the cri-




tical on-site cost, the critical travel cost, the demand relationship




and the number of visits relationship.  The recreational value of each




lake was determined from the demand model by calculating the consumer




surplus which is a function of on-site costs, length of stay per visit,




travel costs and average income.  The resulting per visit value was mul-




tiplied by the estimated number of visits to give a net economic value




for Klamath Lake for 1968 of $82,000.  The relationship derived between




the number of visits to a site and the characteristics of the site was




used as a predictor for percent increase in visits to Klamath Lake if




water quality improved.  New use-intensity ratings were hypothesized




for the lake given a hypothesized two-stage improvement in water quality.




The increase in visits based on the new use-intensity ratings was cal-




culated, and based on this increase, the new economic value was estimated.




The first stage of water quality improvement, removal of algae, would




yield $1.2 million worth of recreation benefits and the second stage,




lower water temperature and beach improvement, would yield an addition




$2.66 million.






    The impact of expanded recreational use of Klamath lake upon the




local economy is estimated through the use of an input-output model of the




Klamath County economy.  The model measures the gross flow of goods and




services between sectors.  A sampling of the sectors of the economy were




surveyed to obtain the necessary detailed financial data for construction
                                    334

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of the transactions matrix.  Data from the demand model were used to




obtain total expenditures in Klamath County associated with recreation




by sector.  The recreation expenditures are viewed as part of final




demand of the input-output model affecting total output and household




income.  Regional recreation benefits for 1968 for Klamath Lake are cal-




culated from the input-output model to be $227,000 of household income.




The hypothesized two-staged improvement in water quality discussed




above would increase household income by $347,820.






    The Reiling, Gibbs, Stoevener study meets criteria C, E, F and G.




They develop a demand function which is used to estimate the recreational




value of each lake studied  (C).  The input-output model is also based on




sound economic principles.  Benefits are quantified in dollars and




secondary benefits to the local economy are also estimated  (E).  The




distributional aspects of the impact of water quality improvements are




addressed by the use of the input-output model which indicates which




sectors of the economy benefit from increased recreation expenditures




(F).  The methodologies used in the study are applicable elsewhere,




although both the recreation survey used to provide data for the demand




model and the survey of the regional economy for the input-output model




would have to be carried out at each location studied  (G).  There would




also have to be agreement on the values assigned to the use-intensity




variables for the methodologies to be used in any comparative manner.







    Criteria A, B and D are not met by the Reiling study.  To implement




either of the methodologies used would require a survey data collection




effort although other study areas might have more readily available






                                    335

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 financial  data for an input-output model  (A).   Water  quality  parameters

 are  not specified  in the  model  (B).   As mentioned earlier,  the  authors

 feel that  a more satisfactory model would  relate  changes  in the physical

 characteristics of the water resource to responses in human behavior

 but  that these data were  not available (D).
 8.    Battelle Memorial  Institute,  "The  Impact of Mine  Drainage Pollution
      on  Industrial Water Users  in  Appalachia," Appendix A  to Acid Mine
      Drainage in Appalachia,  a  report by  the Appalachian Regional
      Commission, Columbus, Ohio, March  1969.
      The Battelle Memorial  Institute conducted a  study to estimate  the

effect of mine drainage pollution on the cost of  water use by  industry

in Appalachia.  The  impact  on regional industrial activity was also

examined.


      The study focused on the effect of mine drainage on production tech-

niques and production costs.  The necessary data  could only be obtained

by visits to industrial plants and by detailed interviews with plant and

company personnel.   Sixty-seven in-plant interviews were conducted  in

six river basins.  The sample of plants to be interviewed was  chosen to

pinpoint those industrial water users most likely to be affected by acid

mine drainage.  This involved collection of data  on the general water use

characteristics and water quality sensitivities of all major Appalachian

industrial water users.  Other data collected included:  the costs  of

water utilization for water supplies, pumping, treatment, distribution,

recirculation and waste treatment; the proportion of water costs to the

overall value of industrial production; methods adopted by industries
                                    336

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to adjust to mine drainage conditions; and costs of adjustments to mine




drainage.






     The economic impact of acid-mine drainage was inferred from the in-




terview data.  Detailed cost estimates were developed for various methods




of treating mine drainage polluted industrial water supplies, including




treatment at the source and lime neutralization.  A hypothetical three-




stage reduction in mine-drainage pollution was assumed and treatment




costs were applied to interview data to obtain estimated savings.  The




following costs and potential savings were investigated:  costs of alter-




native water sources  (savings from substituting raw surface water);




costs of using modified equipment; abnormal operation, maintenance




and replacement costs of production equipment or water-system components;




costs of product adjustment  (savings in product quality control); costs of




treating mine-drainage derived contaminants in withdrawal of direct




supplies of water from mine-drainage rivers; costs of treating mine-drainage




derived contaminants  in water purchased from municipal or other  supplies




affected by acid-mine drainage.  Expected changes in production were also




analyzed, including new levels of output, new location, new products,




new quality of output given  reduced production costs resulting from




reduction in mine drainage.  The results for the  sample were then pro-




jected to include the entire manufacturing sector within each river




basin surveyed.






     The survey  showed the maximum savings from pollution  reduction would




occur from treatment  at the  source rather than  lime  neutralization.   The
                                     337

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maximum possible savings from a 90 percent reduction in mine drainage




at the source in all Appalachian river basins is $1,230,000.  The greatest




portion of the savings come from savings in chemicals used in conventional




methods of water treatment.  The major savings would be to large plants




directly using river water.  Fifty percent of the entire savings would




accrue to several very large steel producing plants in one region of




Pennsylvania.  It was found that adjustments to acid-mine drainage accounted




for only a small fraction of total water costs at manufacturing plants




which themselves were generally less than one percent of the total value




of sales.  The study concluded that no regional industrial impacts includ-




ing water use, production, employment and use of raw materials and power




would occur as a result of reduction in acid-mine drainage.






     The Battelle study meets criteria B, C, D, E, F, and G.  A specific




pollutant, acid-mine drainage, is the focus of this study  (B).  From the




survey data, functional relationships are developed showing the savings




resulting from various levels of pollution reduction depending on the




type of treatment employed (C).  The detailed industry-by-industry in-




vestigative work done for this study was aimed at identifying the economic




impact of a specific pollutant on a specific receptor, the manufacturing




industry in Appalachia (D).  Benefits of pollution reduction are quan-




tified in dollars (E).  Distribution of the savings from mine drainage




reduction was considered for different industry groups and between large




and small industries (F).  The methodology employed in this study can




be applied to other regions,  and in many cases, is the only way to
                                   338

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understand the financial impact on industry of environmental improve-




ments (G).  It, of course, involves expensive detailed interviewing.






     As just mentioned, because of the techniques necessary for data




collection for this type of study, application is not easy and therefore




it does not meet criteria A.









Summary of Reviews






     This assessment of benefit studies has shown that few studies meet




all criteria.  Criterion A, ease of application, proved to be the most




difficult criteria to satisfy.  This is primarily because response to




changes in environmental quality is such a complex subject and there




are few relevant studies.  Three of the studies summarized here do meet




criteria A:  the Tihansky study, and the Dow Chemical Company study, and




the property value study by Day and Gilpin.  In the Tihansky study, the




benefit group chosen, household water supply, and the pollutant, dis-




solved minerals, had generated enough research interest so that there




were data available on which  to develop a damage function relating




pollutant to economic value.  The  Dow Chemical study used data  (where




available) and judgment values where sufficient data were lacking.






     Of the methods in the three Studies satisfying Criterion A, the




property value technique  employed  by Day and Gilpin is most appealing




because it relies on existing (secondary) data, either property tax




assessments or sales prices and census data.  However, there are
                                   339

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shortcomings to the approach, including the difficulties involved in




selecting a site for cross-sectional or time series study, where the




effects of changes in water quality can be isolated.   (For example,




see discussion on pages 6 to 9 in Darroger and Dornbusch, 1973.)




problem with the property value approach is discussed by Binkley and




Hanemann.  They note that if property values rise near a water body




they may fall in an area further away from the water body and simply




knowing how much property values change near the water body will not




allow conclusions regarding change in social welfare.  (See S. Binkley,




W. Hanemann, Urban Systems Research and Engineering, Inc., pages




14-18.)






     The failure of several studies to meet criterion D, pollution level-




value measurement relationship, points to a major problem in benefit




estimation.  The lack of existing data that link pollutant and value




measurement results in the need to conduct surveys or undertake other




expensive data collection efforts.  A study that requires primary data




collection to establish this relationship therefore does not meet cri-




teria A.  Such empirical data for many water quality parameters, and




especially for interactions among water quality determinants, is not




readily available.  Studies which do meet criteria D are the Tihansky,




Battelle Institute, Randall and Crocker studies.  Both the Tihansky and




Battelle Institute studies are concerned with pollutants which affect




the cost of production, the former for the household and the latter for




industry and both are able to specify defensive expenditures for different
                                   340

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levels of pollution.   The Randall and Crocker studies specifically es-




tablish the connection between pollution levels and value measurement in




their surveys.






     Criteria B, consideration of identified pollutants, is a third




area of difficulty with most of the studies considered.  Only the




Tihansky, Dow Chemical Company, and Battelle Institute efforts address




specific pollutants (dissolved minerals, sediment and acid-mine drainage,




respectively).  Other studies focus on more general types of pollution




such as lowered visibility, or rivers and lakes with poor water quality,




and do not develop data or methodologies to handle individual pollutants




or combinations of pollutants.






     Criteria C, E, F and G  (theoretical validity; benefit quantification;




distribution of impacts; and generalizability) are more readily met than




A, B or D.  The Tihansky, Day and Gilpin, Randall, Reiling and Battelle




Institute studies satisfy these criteria.  These studies are based on




accepted methodologies, and they quantify benefits in dollar terms.  They




address the equity question in different ways, including comparing impacts




on different  regions, different income groups, different industries or




sectors of the economy, or different population groups defined by location




or consumption.  The techniques employed in these studies are reproduc-




ible in other locations for other problems, however, most would require




new data collection efforts.  The Crocker study meets  criteria C, E and




G but does not address the equity question.  The Dow Chemical Company




and Oster studies satisfy criteria E, F and G but calculate  aggregate
                                    341

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benefits rather than developing a functional relationship between bene-




fits and levels of pollution.






     From review of benefit methodologies presented here it appears




that there are several approaches for evaluating water quality impacts




from agriculture that could be developed for empirical testing.  Table E-




2 shows which methodologies are most appropriate for particular activi-




ties, uses or groups.  Referring back to the studies reviewed, examples




of methodologies applied to specific benefit categories include:






     1)   time budget - Crocker study of aesthetics;




     2)   bidding games - Randall study of aesthetics and Oster study of




          recreation (a less sophisticated example where aggregate




          willingness-to-pay data is collected);




     3)   travel cost - Reiling study of recreation;




     4)   marginal cost - Tihansky study of household water supply and




          Battelle Institute study of industrial water supply;




     5)   net factor income - Dow Chemical Company study of commercial




          fishing (among other things);




     6)   market study - Day and Gilpin study of property values;




     7)   non-dollar measurement - Day and Gilpin's value ranking




          study and;




     8)   input/output model - Reiling model to estimate local economic




          benefits.
                                    342

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Table E-2.  Comparison of Methodologies to Measure Water Quality Benefits


Methodology
Types
time
budget
bidding
games
travel
costs

marginal
costs

net factor
income
market
study
non-dollar
measurement
input/out-
put model
alternative
cost
Benefit Categories
01
aesthetic

X

X









ranking




e
recreatio

X

X

X







ranking





property
values












X






human
health



X


medical
costs
& lost
earnings








1-1
commercia
fishing










yield
change
x price







01
agricultur










yield
change
x price







2?
municipal
water suppl







treatment
production
costs








^ >i
industria
water suppl







treatment
production
costs









dredging
(navigatior
flood centre








X










ecology













change
in
habitat


cost to
reproduce

local or
regional
economy















X



-------
We have not reviewed a study devoted to valuing water quality benefits




to ecology  (alternative cost); (for a good discussion of the sparseness




of the literature in this area, see Jordening, L., Development Planning




and Research Associates, Inc., pages 47-48).






     As indicated in the above discussion, there are trade-offs involved




in choosing a methodology appropriate for use in estimating benefits to




water quality groups.  The major one is the use of readily available




secondary data versus the need for a theoretically valid model which




relates specific pollutants to a value measurement.  An example of this




tradeoff is the Dow Chemical Company study which resorts to judgment and




aggregate values, due to the lack of required data.  There are more data




available for certain benefit categories such as household water supply




than for others such as aesthetics  (see earlier discussion of Tihansky




study).  Surveys are expensive and time consuming but there does not




appear to be any feasible alternative especially for measuring recreation




or aesthetic benefits which are two of the major categories in which bene-




fits from reducing nonpoint source pollution lie.






     Another related problem is the need to isolate specific pollutants




and to relate them to a value measurement.  Photographs are used in the




two studies concerned with air pollution  (Crocker and Randall), a sedi-




ment load standard is developed in the Dow Chemical Company report, and




dissolved mineral concentration levels are specified in the Tihansky




study.  These are examples of mechanisms employed to match a physical




measure of environmental quality to a measure of value to people.  In




cases where more than one water quality parameter is of interest, as is






                                   344

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often the case for water quality problems, the problem is much more




difficult.  Again there is a trade-off between choosing a methodology




which develops a valid functional relationship and one which examines




benefits in the aggregate.






     Several of the methodologies which we have reviewed can be used to




investigate the distributional aspects of water quality benefits.  For




instance, bidding games can be applied to different population groups




defined by location or income, methods to evaluate the marginal cost




of treatment or production can be used to examine differences in bene-




fits among industry or household groups or among geographic regions, and




the input-output model may be used to focus on impacts to alternative




economic sectors.  The major concern here, of course, is the definition




of equity, the decision to choose certain groups whose welfare is of




enough importance to require the focus of the study.  As we have seen,




many groups are important depending on the region or problem of concern.






     The land/water configuration and land uses of the study area be-




come important factors in determining the appropriate methodology(ies).




Is the water body a large flood control impoundment that is widely used




for recreation or is it a river used for municipal water supply and




industrial cooling water?  Is it a small stream running through agri-




cultural land used by local sport fishermen or is it an estuary used as




a commercial fishery and for navigation purposes.  These kinds of ques-




tions must be answered to determine which impact groups are likely to




derive  the most benefit  from  improvements  in  water  quality.   Choice




of  impact groups  will  in turn reduce  the  number  of  candidates for  bene-
                                   345

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 fit methodology.  If a number of beneficiary categories appear to be

 important then several different instruments may have to be employed

 simultaneously.  This, of course, will increase the scope and expense

 of a benefit study.




  REFERENCES, APPENDIX E

Battelle Memorial Institute.  The Impact of Mine Drainage Pollution on Indus-
     trial Water Users in Appalachia, Appendix A. to Acid Mine Drainage in
     Appalachia.  A report by the Appalachian Regional Commission.  Columbus,
     Ohio, 1969.

Binkley, S. and Hanemann, W.  Urban Systems Research and Engineering, Inc.,
     The Recreation Benefits of Water Quality Improvement;  Analysis of Day
     Trips in an Urban Setting.  Final Report.  U.S. Environmental Protection
     Agency, December 1975.

Crocker, T.D.; Horst, R.K. Jr.; and Schulze, W.  Multidisciplinary Research in
     Environmental Economics;  Two Examples.  A paper prepared for the Workshop
     on Multidisciplinary Research Related to the Atmospheric Sciences, National
     Center for Atmospheric Research, Boulder, Colorado, August 1977.

Darroger, S.N.; Dornbusch, D.N.  Benefits of Water Pollution Control on Property
     Values.  EPA 600/5-73-005, Environmental Protection Agency, October 1973.

Day, J.C. and Gilpin, J.R.  "The Impact of Man-Made Lakes on Residential Pro-
     perty Values:  A Case Study and Methodology Exploration."  Water Resources
     Research, Vol. 10, No. 1, February 1974.

Dorfman, R. and Dorfman, N.  Economics of the Environment, W. W. Norton & Co.,
     1972.

Dow Chemical Company.  An Economic Analysis of Erosion and Sediment Control
     Methods for Watersheds Undergoing Urbanization.  Final report, U.S. Dept.
     of Interior; Midland Michigan, February 15, 1971 - February 14, 1972.

Energy Resources Company, Inc.  Analysis of Construction Grant Funding of
     Wastewater Reclamation Projects.  Interim Progress Report.  U.S. En-
     vironmental Protection Agency, Office of Water Programs Operations,
     Municipal Construction Division, August 19, 1977.

Gibbs, K.C.; Reiling, S.D.; Stover, H.H.  Economic Benefits from an Improve-
     ment in Water Quality.  EPA-Re-73-008.  U.S. Environmental Protection
     Agency, January 1973.
                                     346

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Gramlich, F.W.  Estimating the Net Benefits of Improvements in Charles River
     Water Quality.  Unplublished Ph.  D.  dissertation,  Harvard University,
     March 1975.

Jordening, L., Development Planning and Research Associates, Inc.   Estimating
     Water Quality Benefits.  PB 245-071.  U.S. Environmental Protection Agency,
     Office of Research and Monitoring, August 1974.

Loehman, Edna.  A Model for Valuing Health Effects of Air-Quality Improve-
    ments .  Preliminary Staff Paper 48, University of Florida, Institute
    of Food and Agricultural Sciences, Food and Resource Economics Department,
    Gainesville, Florida, April 1977.

Nathan, Robert R., Associates, Inc.  Mine Drainage Pollution and Recreation
    in Appalachia.  The Appalachian Regional Commission.  Washington, D.C.,
    June  1969.
                                                                         \

Oregon State  University.  The Demand for Non-Unique Outdoor Recreational
    Services;  Methodological Issues.  Technical Bulletin 133, Agricultural
    Experiment Station, Corvallis, Oregon, May 1976.

Oster, Sharon.  "Survey Results on the Benefits of Water Pollution Abatement
    in the Merrimack River Basin."  Water Resources Research, October 1977.

Peskin, H.M.; Seskin, E.P.  Cost Benefit Analysis and Water Pollution Policy.
    URI  77000, Urban Institute, Washington, D.C., 1975.

Stoevener, H.H.; Reiling, S.D.; and Gibbs, K.C.  Economic Benefits from an
    Improvement in Water Quality.  EPA-R5-73-008, U.S. Environmental
    Protection Agency.  Office of Research and Monitoring, Washington, D.C.,
    January  1973.

Tihansky, D.P.  "Damage Assessment of  Household Water Quality," Journal of
    Environmental  Engineering Division, ASCE, Vol.  100, No. EE4, August 1974.

Unger, S.J.  and Jordening, D.L.  Bibliography of Water Pollution Control
    Benefits  and Costs.  EPA 600/5-74-028, Environmental Protection Agency,
    Washington, D.C., October 1974.

U.S. Department of Agriculture.  Benefits of Abating Aesthetic Environmental
    Damage from the Four Corners Power Plant, Fruitland, New Mexico.  Bulletin 618,
    Agricultural Experiment Station, New Mexico State University, Las Cruces,
    New  Mexico, May 1974.

Urban Systems Research and Engineering,  Inc.  The Recreation Benefits of Water
    Quality  Improvement, Analysis of Day Trips in an Urban  Setting.   Final
    report.   U.S.  Environmental Protection Agency,  December 1975.
                                      347

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                                 Appendix F

                        Crop Response to Fertilizer

      One of the policies  evaluated in Section 6 of the report pertains

 to mandatory reduction in the  use of fertilizer as a way to improve

 water quality.   This analysis  provides the basis for estimating yield

 reductions and  farm revenue changes that are treated in Section 6.


      To estimate the effects of fertilizer usage on farm revenues (as

 well as water quality)  it is necessary to relate application levels to

 yields.   Nitrogen and P_O  are the fertilizers of primary interest.*

 The work of Taylor and Fronberg (1)  for Illinois appeared attractive

 because optimum levels of nitrogen application are related to yield

 (expressed as a percent of maximum yields attainable) for a range of

 corn to nitrogen price ratios.  Moreover, small differentials in yield

 are estimated in the range where optimal results are anticipated,**

 i.e.,  where marginal costs and marginal returns are equal.  (Some other

 data,  developed expressly for  Indiana available at the outset of work,

 were considered inadequate because average statewide conditions are

 treated,  rather than specific  counties or soil types relevant to the

 Black Creek area (e.g.,  (4)  and (5)).   If the Illinois yield-nitrogen
*     The K2O fertilizer is not analyzed because crop response and water
quality are less sensitive to potassium than to nitrogen and phosphorus.

**    For example, Taylor and Frohberg list seven nitrogen application
rates which cover a range of corn yields from 100 percent of maximum
yield to 99.1 percent.
                                     348

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response relationships could be made applicable to Indiana, we would be




able to investigate conditions where relatively large reductions in




nitrogen (e.g., 14 percent) applications result in small reductions in




yield (e.g., one percent).






     Data on corn response to nitrogen for Indiana were then obtained




from Meta's field work (2, 3).  Tests had been carried out for a range




of nitrogen applications from 0 to 180 pounds per acre on Blount Silt




Loam and 0 to 210 pounds per acre on Odell Silt Loam on the two differ-




ent soil types identified as relevant to Allen County  (2, 3).  However,




the test results are of limited value because only two intermediate levels




between zero and maximum nitrogen application are reported.  A comparison




of Indiana data with Taylor/Frohberg (1) was made to see if the relation-




ship developed for Illinois could be applied to the Indiana Odell Silt




Loam soil and thus establish a more precise estimate of yield response to




nitrogen in the range of near maximum yielded conditions, i.e., where only




small yield reductions occur with sizeable reductions in nitrogen appli-




cation.   Fig.  F-l  shows the comparison between Illinois crop response




(1) and that for Indiana on one type of soil (3).  The four data points




provided by the Indiana tests (shown for three different applications of




P 0_) indicate a fundamental difference in the Indiana crop response




compared to Illinois.  At low rates of nitrogen application (0 to 1.0




pounds nitrogen per bushel of yield), yield improvements are greater




on the Indiana soils than on the Illinois soils.  Also it is seen that




maximum yield in Illinois occurs with 1.34 pounds nitrogen per bushel
                                    349

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           .2
.4       .6       .8       1.0      1.2
 NITROGEN APPLICATION PER BUSHEL OF YIELD
                                                          1.4
1.6
1.8
Figure F-l.   Comparison  of Indiana Tests  (1967-&9) to  Illinois  (Taylor-
              Frohberg) in Corn-Nitrogen Response
                                     350

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yield while maximum yields in Indiana occur at application rates be-

tween 1.43 and 1.67 depending on the level of P 0  application.*  Thus,
                                               *£ j

for maximum yield in Indiana on Odell Silt Loam soil of 130 bu/acre, the

Illinois response function would estimate a nitrogen application rate

of 174 Ibs/acre  (1.34 Ibs x 130) whereas Indiana tests indicate 185.9

to 217.0 Ibs/acre are needed.


     The yield response data from Reference  (1) were therefore judged

unsuitable for Indiana Odell Silt Loam.  However, the Illinois response

function was utilized in the subsequent steps for Odell Silt  Loam as an

aid in approximating the general shape of the Indiana response function

because only four nitrogen application rates are reported from the

Indiana Tests.  E'or the Blount Loam soil, the Illinois response function

was ignored; the yield response to nitrogen on Blount Loam soil is even

more divergent from the Illinois function than the Odell Silt Loam soil.


     For the Odell Silt Loam (used for soil types 4 in Black Creek),

corn response for applications of 0, 70, 140 and 210 pounds of nitrogen

are reported for four different rates of P (i.e., 0, 17.6, 35.2, 52.8 Ibs

per acrs).  Average yield over the period 1967 to 1969 is plotted as a

function of P for the four nitrogen application levels as shown in

Fig.  F-2.  A cross plot of yield versus nitrogen application was then

                                                           **
made for three specific rates of P~O  as shown in Fig.  F-3.      Fig.  F-3
                                  £• O
*     In Reference  (1) phosphorous and potassium application rates are
assumed to be equal to the amounts removed in the grain which  should
approximately maintain the P and K levels in the soil  and  thus  the yield
response is essentially dependent only on the amount of nitrogen  applied.

**    Where P =  .44  (P O ).


                                    351

-------
   150
  140
  130
  120
                     7
      7
  no
  100
   90
   80
                         litr
                             ?gen
                                 app
led
fib
                                             .acr
                                                      0.
                   • 4P.
   70
   60
   50
   40
     0       10      20      30      40      50      60
                      phosphorus applied (Ib/acre)
Figure F-2.  Yield Response of Corn to Fertilizers (Odell Silt Loam)
                                     352

-------
Ul
                140
                130
                120
                110
                100
             UJ
             tc
             u
             g  ^
             a

             uj
                80
                 70
                 60
                 50
                       S14 ) BU> \CRE
                                TI vi n
FFOHB

ORVE
                                   'MA
  X^
                                       RG

                                                      ^
                               = KDLE
                                 iOTt

                                 OLf
                                -L
                                                                     /ACF
                                           met
                                           /ACFi
20      40     60     80     100     120     140

                   NITROGEN  APPLIED  (LB/ACRE)
                                                                               160
                                                      180
200    220
                       Figure F-3.   Yield Response of Corn to Fertilizers(Odell  Silt Loam)

-------
includes the Taylor/Frohberg response function which served as a guide

for interpolating between the four data points reported in the Indiana

tests.  Nitrogen application of 160 Ibs/acre and P2O5 of 30 Ibs/acre

were recommended to achieve an expected yield of 130 bu/acre in the

Black Creek area.*  This is in close agreement with the yields (131 bu/

acre) obtained from the P~0  crossplot and shown inFi
-------
(soil type 3) the recommended N is 160 Ibs/acre and 30 Ibs/acre of P-jO^


with yields in the Black Creek area expected to be 130 bu/acre.  For


uplands  (soil type 1) the recommended N is 140 Ibs/acre and 30 Ibs/acre


of P~05 for an expected yield of 120 bu/acre.  From  Fig.  F-4 it is seen


that with N = 160 for lowland soils, the yield is the same as at N = 120,


the yield at other levels of nitrogen application is obtained by multi-


plying the response function value (i.e., percent of yield at 120 lbs/


acre nitrogen) by 130 bu/acre.




     For upland soils at N = 140, Fig.  F-4 shows that expected yield


is 1.022 times the yield at N = 120.  Since we force the relationship


of 120 bu/acre yield at N = 140 to comply with Galloway's estimate,


the reference yield  (at 120 Ibs/acre of nitrogen) must be reduced to


117.4 bu/acre (i.e., 120 bu/acre * 1.022).




     All the above calculations for soil types 1 and 3 are based thus


far on the yield-nitrogen response data which are reported to a fixed


level of P Cv of 120 Ibs/acre.  We next must adjust the derived yield-
          
-------
to obtain the yield-nitrogen response curves  for  P~0  = 30 and 20 respec-



tively.  The final response curves  shown  in  Fig.  F-5 for soil types 1



and 3 have, therefore, been derived from  Fig. F-4 but with adjustments to



incorporate applications of P_O  and nitrogen to  give the expected yields



recommended to Meta Systems (by Galloway)  for soil types 1 and 3.





     To investigate the sensitivity of water  quality to various fertili-



zation levels based on the yield response relationships, changes in nitro-



gen and P_0  application were postulated  and  applied to the derived yield
         £ 3


response functions.  Decreases in nitrogen levels of 13 percent from rates



recommended by Galloway would be desirable according to Commoner (6)  to



reduce nitrate concentrations for surface water for the East Central region
"


O
GO
9

8
>-
s
                        77
20      40      60      80      100

            NITROGEN APPLIED (LB/ACRE)
                                               120
                                                        140
                                                   160
                                                                      180
Figure F-4.  Yield Response of Corn to Nitrogen

             Silt Loam,  o = Data Points from Ref. 3.
                                                        120  Ib/acre).  Blount
                                   356

-------
of Illinois.  Commoner indicates that if the rate of  fertilizer  application




were reduced to 146 kg of N per hectare corn  (from  a  level of  168  kg  of  N




per hectare), the 10 ppm standard would be exceeded no more  than five per-




cent of the time during the spring months.






     In addition, two cases were postulated to evaluate  the  impacts on




yield from changes per acre reduction in nitrogen which  is a lesser re-




duction than dictated in P_0  application to corn.  The  changes  in the




recommended P O  level were stipulated on an arbitrary basis.  The




recommended levels of P-O^ were increased and decreased  in 10  pound




increments.  In preliminary studies, these changes  in phosphorus fer-




tilization rates were found to have negligible impacts on water  quality




and therefore were not considered further.
                          NITROGEN APPLIED (LB/ACRE)



 Figure F-5.   Yield Response to Nitrogen (Blount Silt Loam)
                                     357

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REFERENCES, APPENDIX F


1.    Taylor, C.  Robert and Klaus K.  Frohberg,  "The Welfare Effects of
     Erosion Controls, Banning Pesticides and  Limiting Fertilizer
     Applications in the Corn Belt," American  Journal of Agr.  Econ.,
     February 1977.

2.    Stivers, R. K., et al.,  "Nitrogen, Phosphorus and Potassium Ferti-
     lization of Continuous Corn on Blount Silt Loam, 1962-1965,"
     Department of Agronomy and AES Farms, Purdue University Agricultural
     Experiment Station Research Progress Report:  299, March 1967.

3.    Stivers, R. K., et al.,  "Response of Corn to Fertilization on Odell
     Silt Loam,  1967-1969," Research Progress  Report:  385, February
     1971.

4.    "Crop Yield Response to Fertilizer in the United States," U.S.
     Department of Agriculture Statistical Bulletin No. 431, August
     1968.

5.    Spies, C. D., "Corn Fertilization," Agronomy Guide, Cooperative
     Extension Service, Purdue University AY171.

6.    Commoner, B., "Cost-Risk-Benefit Analysis of Nitrogen Fertilization:
     A Case History," Ambio,  62-3, 1977.
                                    358

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                                   TECHNICAL REPORT DATA
                           (Please read Instructions on the reverse before completing)
 . REPORT NO.
 EPA-600/5-79-009
                                                           3. RECIPIENT'S ACCESSION NO.
4. TITLE ANDSUBTITLE
Costs  and Water Quality Impacts  of Reducing Agricultural
Nonpoint Source Pollution:  An Analysis Methodology
            5. REPORT DATE
              August 1979  issuing date
            6. PERFORMING ORGANIZATION CODE
 . AUTHOR(S)
                                                           8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Meta  Systems, Inc.
10  Holworthy Street
Cambridge, Massachusetts  02138
             10. PROGRAM ELEMENT NO.
               1BA609
             11. CONTRACT/GRANT NO.
               R805036-01-0
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Research Laboratory—Athens, GA
Office of Research and Development
U.S.  Environmental Protection Agency
Athens, Georgia  30605
                                                           13. TYPE OF REPORT AND PERIOD COVERED
               Final,  8/77-9/78
             14. SPONSORING AGENCY CODE

               EPA/600/01
15. SUPPLEMENTARY NOTES
16. ABSTRACT
       This study addresses  the  problem of analyzing nonpoint source pollution impacts
from agriculture.  A methodology for regional-level planning is presented that, with
further refinement, could prove of significant value  for  broad analyses of large  num-
bers of policy alternatives,  including best management  practices.  The analytical me-
thod developed allows the simultaneous examination of the water quality impacts of
selected agricultural practices and the economic effects  that alternative practices  and
nonpoint source pollution control policies have on the  farmer.  The nonpoint source  pol
lution control problems that  the methodology addresses  are limited to those that  are
amenable to solution by incremental on-farm adjustments for damage reduction.  The pro-
posed methodology includes  a  farm model, a water quality  model, and a qualitative ap-
proach for the assessment of  the social and economic  impacts of water quality changes
on downstream users.  It may  be applied to evaluate government nonpoint source pollu-
tion control policies and the effects of alternative  agricultural futures.  The method-
ology's use for these purposes  is evaluated through an  illustrative example based on
data from the Black Creek watershed in Northeastern Indiana and a synthesized down-
stream impoundment.
17.
                                KEY WORDS AND DOCUMENT ANALYSIS
                  DESCRIPTORS
                                              b.lDENTIFIERS/OPEN ENDED TERMS
                             COS AT I Field/Group
Water pollution
Agricultural economics
Planning
Analysis
                             02B
                             68D
                             91A
18. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (This Report)
   UNCLASSIFIED
21. NO. OF PAGES
     367
                                              20. SECURITY CLASS (Thispage)
                                                  UNCLASSIFIED
                           22. PRICE
EPA Form 2220-1 (9-73)
                                             359
                                                                    ft U.S. GOVERNMENT PRINTING OFFICE: 1979 -657-060/5381

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